omkarfadtare / Practical_data_science

These are the handwritten notes on Coursera's Practical data science specialization course.

Turning data science project ideas into real-world solutions using AWS cloud:

Prerequisites for this project:

• Proficiency in Python and SQL programming.
• Familiarity with building neural networks using deep learning Python frameworks like TensorFlow or PyTorch.
• Basic understanding of building, training, and evaluating machine learning models.

Brief introduction to Artificial intelligence and Machine learning:

• Artificial intelligence is generally described as a technique that lets machines mimic human behaviour.
• Machine learning is a subset of AI, that uses statistical methods and algorithms that can learn from data, without being explicitly programmed.
• Deep learning is yet another subset of ML, that uses artificial neural networks to learn from data.
• Natural language processing (NLP) or Natural language understanding (NLU) is yet another subset of ML focused on the interaction between computers and humans through natural language, which includes machine translations, sentiment analysis, question answering system, etc.
• Computer vision is a subset of ML that enables computers to interpret and make decisions based on visual data from the world, where you need to classify images into pictures of dogs and cats, distinguish between speed signs and trees.
• Data science is an interdisciplinary field that combines business and domain knowledge with mathematics, statistics, data visualization, and programming skills.

Benifits of performing data science projects on the cloud:

• Data science projects are often geared towards handling massive datasets that can originate from social media channels, mobile and web applications, public or company internal data sources, and more, depending on the specific use case. This data is often messy, potentially error-ridden, or poorly documented. Cloud-based data science tackles these issues by providing tools to analyze, and clean the data, extract relevant features, and gain insights from these large datasets.
• Developing and running data science projects on the cloud offers agility and elasticity. In contrast, if you develop data science projects in a local notebook or IDE environment, such as on your laptop or a company-owned server pool, you are limited by the existing hardware resources. You have to carefully monitor how much data you process and the available CPU processing power for training and tuning your model. If you need more resources, you must purchase additional computing resources, which hinders quick development and flexibility. Additionally, your model training might take too long because it consumes all the CPU resources of the compute instance you have chosen. Using the cloud services, you can indeed switch to a compute instance with more CPU resources or even switch to a GPU-based compute instance.
• The cloud allows you to train your model on a single CPU instance, which is referred to as scaling up, or perform distributed model training in parallel across various compute instances, which is referred to as scaling out.
• In the cloud, when the model training is completed, the instances are terminated, meaning you only pay for what you actually use. The cloud allows you to store and process almost any amount of data and offers a large toolbox of data science and machine learning tools to perform tasks quickly and efficiently.

Project use case: Multi-Class Classification for Sentiment Analysis of Product Reviews

• Assume you work at an e-commerce company that sells various products online. Customers leave product feedback across multiple online channels, such as sending emails, writing chat FAQ messages on your website, calling your support center, or posting messages on your company's mobile app, popular social networks, or partner websites. As a business, you want to capture this customer feedback quickly to spot any changes in market trends or customer behaviour and be alerted to potential product issues.
• Your task is to build an NLP model that takes these product reviews as input. You will use the model to classify the sentiment of the reviews into three classes: positive, neutral, and negative. This classification will help you spot changes in market trends or customer behaviour and be alerted to potential product issues.
• Multi-class classification is a supervised learning task, so you must provide your classifier model with examples to correctly learn how to classify product reviews into the respective sentiment classes. You can use the review text as the input feature for model training and assign sentiment labels to train the model. Sentiment classes are typically represented as integer values during model training, such as 1 for positive sentiment, 0 for neutral sentiment, and -1 for negative sentiment.

Data ingestion and Exploration:

• Data ingestion is the process of bringing data from various sources, such as databases, files, or websites, into a system where it can be used. Think of it like gathering ingredients in your kitchen before cooking a meal.

• Imagine your e-commerce company is collecting customer feedback from multiple online channels, including social media, support centre calls, emails, mobile apps, and website data.

• To accomplish this, you need a flexible and scalable repository capable of storing different file formats, including structured data like CSV files and unstructured data like support centre call audio files. Additionally, it should dynamically scale storage capacity as new data streams in. Cloud-based data lakes provide a solution to this problem.

• Data lake is a centralized and secure repository that can store, discover, and share virtually any amount and type of data. Data can be ingested into a data lake in its raw format without prior transformation. Whether it's structured relational data in CSV or TSV file formats, semi-structured data like JSON or XML files, or unstructured data such as images, audio, and media files, it can all be ingested. Additionally, you can ingest streaming data such as continuous log file feeds or social media channel feeds into your data lake.

• Effective governance is indeed crucial for managing a data lake, especially given the continuous influx of new data. Implementing mechanisms for discovering and cataloguing incoming data helps ensure that the data lake remains organized, accessible, and compliant with regulatory and organizational standards. This governance framework also facilitates efficient data utilization, enhances data quality, and supports data analytics and decision-making processes within an organization. Cloud services facilitate scalable storage, automated data ingestion, robust security and compliance measures, advanced analytics capabilities, cost efficiency, and seamless integration with machine learning, all crucial for effectively managing a data lake.

• There are three types of storage technologies used in computer systems and data storage solutions:

  File storage is a storage technology that organizes data as individual files stored in a hierarchical directory structure, similar to how files are organized on personal computers or file servers. File storage is suitable for structured and semi-structured data such as documents, spreadsheets, multimedia files, and application data.

  Block storage is a storage technology that manages data as individual blocks or chunks at the disk level, accessed using block-level protocols such as SCSI (small computer system interface) or Fibre Channel. It is commonly used in storage area networks (SANs).

  Object storage is a storage architecture that manages data as objects, each consisting of data, metadata (information that describes the object), and a unique identifier. Object storage is suitable for scalable and distributed storage of unstructured data like images, videos, backups, and log files.

• Amazon S3 is commonly used as the underlying object storage for data lakes due to its durability, availability, and scalability. It allows ingestion of vast amounts of data, ranging from small datasets to exabytes. AWS provides tools and services that ensure data lakes built on Amazon S3 are secure, compliant with regulations, and auditable. This includes features like access control, encryption, data governance, and compliance certifications.

• Amazon S3 serves as a centralized repository in data lakes, facilitating easy access and analysis of data for data warehousing analytics. This involves analyzing large volumes of structured data to derive insights and support decision-making. It also supports integration with machine learning tools for extracting deeper insights, identifying patterns, and making predictions based on business requirements.

• Data lakes and data warehouses are indeed different technologies with distinct architectures and purposes. Data lakes are designed for storing vast amounts of raw and unstructured data, enabling flexible data exploration and analysis. In contrast, data warehouses are optimized for querying and analyzing structured data to support business intelligence and reporting. Difference between Data lake and Data warehouse:

Data lake	Data warehouse
- Data lakes are repositories that store vast amounts of raw, unstructured, and structured data in its native format.	- Data warehouses are structured repositories that store processed and organized data in a structured format.
- They are designed to ingest and store data from various sources without prior transformation.	- Data undergoes extraction, transformation, and loading (ETL) to clean, transform, and integrate data from different sources before loading into the warehouse.
- Ideal for exploratory data analysis, data science, and big data processing, allowing organizations to retain raw data for future analysis and insights.	- Optimized for high-performance analytics, reporting, and business intelligence (BI) applications, supporting historical analysis and decision-making.
- AWS S3 Bucket serves as a common example of a data lake storage solution.	- AWS Redshift is an example of a data warehouse service optimized for querying and analyzing structured data.

• AWS Data Wrangler is an open-source Python library focused on simplifying data preparation and exploration for analytics and machine learning tasks. It provides easy-to-use functions and abstractions for working with data in Pandas DataFrames, facilitating common data engineering tasks. AWS Data Wrangler seamlessly integrates with AWS services such as Amazon S3, Amazon Redshift, Amazon Athena, and Amazon Glue, enabling smooth data integration, processing, and interaction between Python environments (such as Jupyter notebooks) and AWS data services. For instance, it simplifies loading data from S3 into Pandas DataFrames for analysis or machine learning and allows pushing processed data back into AWS services like S3 or Redshift.

Code to retrieve data directly from s3 bucket to Pandas DataFrame using AWS Data Wrangler:

!pip install awswrangler
!pip install boto3 # Boto3 is the AWS SDK (software development kit) for Python. It allows Python developers to write software that makes use of AWS services like S3, EC2, DynamoDB, and many more. Boto3 provides an easy-to-use, object-oriented API, as well as low-level access to AWS services.

import awswrangler as wr
import boto3
import pandas as pd

# Replace with your actual AWS credentials
aws_access_key_id = 'your_access_key_id'
aws_secret_access_key = 'your_secret_access_key'
region = 'your_aws_region'

# Create a boto3 session with your credentials
boto3_session = boto3.Session(
    aws_access_key_id=aws_access_key_id,
    aws_secret_access_key=aws_secret_access_key,
    region_name=region
)

# Replace with your actual S3 path
s3_path = 's3://your-bucket-name/path/to/your/file.csv'

# Use AWS Data Wrangler to read data from S3
df = wr.s3.read_csv(path=s3_path, 
                    boto3_session=boto3_session,
                    skiprows=0,
                    nrows=None,
                    dtype=None)

# skiprows: number of rows to skip from the beginning
# nrows: number of rows to read ('None' to read all)

print(df.head())

• AWS Glue is a fully managed service from AWS designed for extract, transform, and load (ETL) tasks, automating processes such as data discovery, cataloguing, cleaning, and transformation. It simplifies ETL job creation and management through a graphical interface, allowing users to schedule and monitor jobs easily. AWS Glue seamlessly integrates with various AWS services like Amazon S3, Amazon RDS, and Amazon Redshift, facilitating efficient data integration and processing workflows across diverse data sources and formats.
• AWS Glue Crawler is a tool provided within AWS Glue that automates the process of discovering and cataloguing data stored in different sources such as Amazon S3, databases, and data warehouses. It scans these data sources, infers the schema of the data (i.e., its structure and format), and then creates metadata tables in the AWS Glue Data Catalog. This allows AWS users to easily access and query the data using AWS Glue ETL jobs or other AWS analytics services like Amazon Athena, Amazon Redshift Spectrum, and Amazon EMR. The AWS Glue Crawler simplifies the management of data cataloguing and ensures that metadata remains updated as new data is added or existing data changes, thereby supporting efficient data integration and processing workflows within AWS environments.
• AWS Glue Data Catalogue serves as a central metadata repository within AWS, storing comprehensive metadata information about databases and tables across your AWS environment. It operates independently as a persistent metadata store, managing both structural and operational metadata for all data assets. This includes storing definitions for tables, partitions, and other relevant metadata components, providing a consolidated and unified view of your data assets. The AWS Glue Data catalogue integrates seamlessly with AWS Glue itself, leveraging its metadata for tasks such as Extract, Transform, and Load (ETL) jobs. Furthermore, other AWS services like Amazon Athena, Amazon Redshift Spectrum, and Amazon EMR utilize the AWS Glue Data catalogue for efficient querying and processing of data, ensuring consistent and reliable access to metadata across different AWS services and environments.
• While AWS Glue uses the Data Catalog to store metadata, they serve different primary functions: AWS Glue executes ETL tasks, while the Data Catalog manages and stores metadata about those tasks and data assets.
• AWS Athena is an interactive query service provided by Amazon Web Services (AWS) that allows you to analyze and query data stored in Amazon S3 using standard SQL. It enables you to run ad-hoc queries on large amounts of data without needing to set up or manage any infrastructure. Athena is serverless, meaning there is no need for provisioning or scaling of resources, and you pay only for the queries you run. It supports a wide range of data formats, including CSV, JSON, Parquet, and ORC, making it versatile for analyzing different types of data stored in S3.
• An ad-hoc query is a query that you write on the fly whenever you need to quickly get specific information from a database or data source. It's like asking a question directly to the data to get immediate answers, without needing to plan or save the query for future use.

Code to execute a SQL query on AWS Athena using AWS Data Wrangler, and loading the results into a Pandas DataFrame:

import awswrangler as wr
import pandas as pd

# Set up the boto3 session for AWS Data Wrangler
aws_access_key_id = 'your_access_key_id'
aws_secret_access_key = 'your_secret_access_key'
region = 'your_aws_region'

# Set up the boto3 session for AWS Data Wrangler
wrangler_boto3_session = wr.Session(
    aws_access_key_id=aws_access_key_id,
    aws_secret_access_key=aws_secret_access_key,
    region_name=region
)

# Create an S3 bucket for Athena query results (if it doesn't exist)
wr.athena.create_athena_bucket(boto3_session=wrangler_boto3_session)

# Define your SQL query
sql_query = "SELECT * FROM your_database.your_table LIMIT 10"

# Specify the database
database = "your_database" 

# Execute the query and read results into a DataFrame
df = wr.athena.read_sql_query(
    sql=sql_query,
    database=database,
    boto3_session=wrangler_boto3_session
)

print(df.head())

Data visualization:

• Pandas is indeed an open-source library used for data analysis and manipulation in Python. It provides powerful data structures like DataFrame and Series, along with tools for reading and writing data between in-memory data structures and various file formats.
• NumPy is another open-source library that supports scientific computing in Python. It provides powerful numerical arrays and functions for operations on these arrays, which are essential for numerical computations and data analysis.
• Matplotlib is a widely used library for creating static, animated, and interactive visualizations in Python. It provides a variety of plotting functions to visualize data in different formats, ranging from simple line plots to complex 3D plots.
• Seaborn is built on top of matplotlib and enhances data visualizations with statistical data analysis capabilities. It provides a high-level interface for drawing attractive and informative statistical graphics, making it easier to create complex visualizations with fewer lines of code.

Statistical bias and Feature importance:

• Statistical bias and feature importance are indeed critical tools for evaluating the quality of your data and understanding the role each feature plays in your model. Statistical bias helps you identify any systematic errors in your data, while feature importance helps you understand which features significantly contribute to your model's predictions.

• Statistical bias refers to a systematic error or deviation from the true value or an expected value in the data or a model's predictions. This bias results from the data collection process, model assumptions, or other factors that cause the estimates to be consistently inaccurate in a particular direction.

• Feature importance refers to the measure of the impact or significance of each feature in a dataset on the predictions made by a machine learning model. It evaluates how much each feature contributes to the model's predictive power. Feature importance provides a ranking of features based on their contribution to the model's performance. This ranking helps in understanding which features are most influential in making predictions and which ones have less impact.

• For example, In fraud detection, if fraudulent transactions are rare in the dataset, the model may not perform well in identifying fraud due to insufficient examples of fraudulent transactions. One common solution is to augment the training dataset with more fraudulent examples, which can help the model learn to identify fraud more effectively.

• Similarly, In our use case, If one product category (e.g., category A) has significantly more reviews than others (e.g., categories B and C), the model may become biased towards predicting sentiments accurately for category A while performing poorly for categories B and C. This happens because the model has more information and training data for category A, making it less effective for underrepresented categories.

• There are various ways in which bias can be introduced in the dataset:

  Activity bias arises when certain groups or individuals are overrepresented or underrepresented in the data due to their level of engagement or activity. For example, in an online shopping dataset, frequent users might have more recorded data about their preferences and behaviours compared to occasional users. This can lead to biased predictions, as the model may learn more about the frequent users and thus be less accurate for occasional users.

  Societal bias reflects existing societal inequalities and prejudices that are mirrored in the data. This type of bias can lead to unfair treatment of certain groups. Historical biases against certain demographics, such as race or gender, may be embedded in the datasets, resulting in biased outcomes in areas like hiring or lending. For instance, if historical hiring data is biased against a particular gender, a model trained on this data may also discriminate against that gender.

  Selection bias occurs when the data collection process systematically favours certain samples over others, resulting in an unrepresentative dataset. For instance, if a survey is conducted only among tech-savvy individuals, it may not accurately capture the opinions of the general population. This can lead to biased conclusions, as the sample is not representative of the entire population.

  Measurement bias happens when there are inaccuracies in data collection methods, leading to systematically incorrect measurements.

  Sampling bias results from a non-random sample of a population, leading to certain groups being over or under-represented.

  Response bias arises when respondents provide inaccurate or false information, often influenced by the question's wording or the survey environment.

Drift:

• Drift refers to any change or deviation in the statistical properties or distribution of data over time that can impact the performance of models. These changes can occur in various forms, affecting different aspects of the data and the model's behaviour:

  Data drift occurs when the statistical properties of the data used by a model change over time. This can make the model less accurate because it was trained on data with different characteristics. For example, if the features of the data (like user behaviours or product preferences) change, the model's performance may degrade.

  Concept drift refers to a change in the relationship between the input and output variables over time. For instance, if a model predicts product purchases based on age and the purchasing behaviour of different age groups shifts, the model's predictions will become less accurate..

  Covariate drift happens when the distribution of the predictor variables (features) changes over time. For example, if temperature was a key predictor for ice cream sales, and the relationship between temperature and sales changes (people start buying more ice cream on colder days), the model's predictions would be affected.

  Prior probability drift occurs when the frequency of the target outcomes changes over time. For instance, if a coin flip initially results in heads 70% of the time and later changes to a 50-50 distribution, a model trained on the initial data will be less accurate in predicting future outcomes.

  Model drift refers to the phenomenon where a model that used to perform well becomes less accurate over time. This can happen due to changes in the data distribution, changes in the real world that affect the relationships the model learned, or other factors that make the original model less relevant.

  Population drift occurs when the population on which the model is applied changes over time. For example, a model trained on movie preferences in one country may not perform well if applied to a different country with different preferences.

  Label drift happens when the meaning of the target labels changes over time. For example, if the criteria for labelling data as "cat" or "dog" change, the model will struggle to learn and make accurate predictions because the definitions of the labels are inconsistent.

Matrics to measure imbalance in data:

• Class imbalance refers to a situation in a classification problem where the number of instances in each class is not evenly distributed.
• Difference in Proportions of Labels (DPL) calculates the absolute difference in the proportions of particular outcomes (e.g., positive vs. negative reviews) between different groups or categories within a dataset. This helps to quantify the degree of imbalance or disparity in outcomes across these groups. DPL helps to understand whether there are imbalances in outcomes across different groups or categories. In the context of a product reviews dataset, understanding the DPL between different product categories is crucial. For instance, if some categories receive disproportionately more positive reviews than others, this insight can inform various strategic decisions such as marketing efforts, product development, and resource allocation within a company. While Cumulative Incidence (CI) or overall reviews look at the total number of reviews and their general trends, DPL specifically focuses on whether some categories get higher or lower ratings than others. This targeted analysis is useful for identifying specific areas where there might be an imbalance. Consider a dataset of customer reviews for an e-commerce platform where each review is labelled as either "positive" or "negative". By calculating the DPL, you can determine if certain product categories receive more positive (or negative) reviews compared to others, highlighting potential biases or areas for improvement.
• SHapley Additive exPlanations (SHAP) values can also be used to assess feature importance by averaging the absolute SHAP values for each feature across all predictions. It is a method for interpreting individual predictions from machine learning models. They aim to explain the output of a machine learning model by attributing the prediction outcome to different features in the input data. SHAP values are based on the idea of Shapley values in cooperative game theory. Shapley values calculate the contribution of each player (feature) in a coalition (subset of features) to the overall outcome (prediction).

Tools to detect Statistical bias in the dataset:

• Amazon SageMaker Data Wrangler is a powerful tool designed to simplify the process of data preparation and feature engineering for machine learning. It is part of Amazon SageMaker, a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly and easily. AWS SageMaker Data Wrangler offers a visual interface that allows users to interactively prepare their data without writing code. It provides a wide range of built-in data transformers and recipes that simplify common data preparation tasks. These include handling missing values, encoding categorical variables, scaling features, and more. SageMaker Data Wrangler seamlessly integrates with various AWS data sources and services, such as Amazon S3 for data storage, Amazon Athena for querying data in S3 using SQL, Amazon Redshift for data warehousing, and AWS Glue for ETL (Extract, Transform, Load) jobs. The tool includes automated data profiling capabilities that help users understand their datasets better. This includes summary statistics, data distribution visualizations, and insights into potential data quality issues. Users can visualize their data and transformations to gain insights into how features are modified and how data distributions change after processing steps.
• Amazon SageMaker Clarify is indeed a critical tool that helps detect biases in both training data and model predictions. It provides model explainability through tools like SHAP values, which highlight the contribution of each feature to predictions, aiding in understanding model behaviour. SageMaker Clarify generates bias reports that include statistical analyses such as disparate impact analysis and fairness metrics across different demographic groups defined by sensitive attributes (e.g., race, gender). These reports offer insights into biases present in the model and its predictions. SageMaker Clarify not only detects biases but also performs bias detection in both trained and deployed models, ensuring ongoing monitoring and mitigation of biases throughout the model's lifecycle.

Code to detect statistical bias using Amazon SageMaker Clarify:

import sagemaker
from sagemaker import clarify

# Specify your SageMaker execution role and session
role = sagemaker.get_execution_role()
session = sagemaker.Session()

# Define the S3 bucket and prefix for input and output data
bucket = 'your-s3-bucket-name'
prefix = 'sagemaker/clarify'

# Specify the path to your training dataset in S3
train_data_uri = f's3://{bucket}/{prefix}/train_data.csv'

# Specify the path to your model artifacts in S3
model_uri = f's3://{bucket}/{prefix}/model.tar.gz'

# Specify the S3 path for bias report output
bias_report_output_path = f's3://{bucket}/{prefix}/bias_reports'

# Create a SageMaker Clarify processor
clarify_processor = clarify.SageMakerClarifyProcessor(role=role,
                                                      instance_count=1,
                                                      instance_type='ml.m5.large',
                                                      sagemaker_session=session)

# Specify configuration for bias detection job
bias_config = clarify.BiasConfig(
    label_name='your_label_column_name',
    facet_name='your_sensitive_attribute_column_name',
    group_name='your_group_id_column_name',
    # Additional configuration options can be set here, such as reference_groups and probability_threshold
)

# Specify data configuration
data_config = clarify.DataConfig(
    s3_data_input_path=train_data_uri,
    s3_output_path=bias_report_output_path,
    label='your_label_column_name',
    headers=train_data.columns.to_list(),  # Optional: Provide headers if your dataset has headers
    dataset_type='text/csv'
)

# Run bias detection job
clarify_processor.run_bias(data_config=data_config,
                            data_bias_config=bias_config,
                            model_uri=model_uri,
                            pre_training_methods='all',
                            post_training_methods='all')

# Wait for the job to finish
clarify_processor.wait()

Amazon SageMaker Data Wrangler	Amazon SageMaker Clarify
- SageMaker Data Wrangler is designed for data preparation, transformation, and feature engineering.	- SageMaker Clarify is used primarily for bias detection, explainability, and fairness assessment in machine learning models.
- Use SageMaker Data Wrangler when you need to integrate and preprocess data from multiple sources, benefiting from its visual interface and efficient data handling across distributed locations.	- Use SageMaker Clarify if your priority is to analyze biases and ensure model fairness across a vast dataset, leveraging its scalability and robust bias detection capabilities.

AutoML:

• AutoML (Automated Machine Learning) refers to the process of automating the end-to-end process of applying machine learning to real-world problems. It includes automating tasks such as data preprocessing, feature selection, model selection, hyperparameter tuning, and model evaluation.

• AutoML makes machine learning accessible to those without extensive expertise by providing a graphical interface or easy-to-use APIs, automating repetitive tasks, and speeding up the model development process while efficiently utilizing computational resources. It ensures standardized procedures for model training and evaluation, facilitating reproducible results through standardized workflows. Often resulting in better-performing models, AutoML systematically and thoroughly tunes hyperparameters and selects models, employing cutting-edge algorithms and methodologies without requiring users to stay updated with the latest advancements.

• AutoML can be integrated at various stages of the machine-learning workflow:

  Data Preprocessing: AutoML includes automated cleaning, which handles missing values, outliers, and data normalization, ensuring the dataset is ready for analysis. Additionally, it performs feature engineering by automatically generating and selecting important features, transforming and encoding data, optimizing the dataset for better model performance, and identifying and selecting the most relevant features for modelling.

  Model Selection: AutoML involves algorithm selection, where it chooses the most appropriate algorithms for the dataset, and the use of ensemble methods, which combine multiple models to enhance overall performance. It automates the process of training multiple models and selecting the best one.

  Hyperparameter Tuning:: AutoML involves optimization techniques such as grid search, random search, and Bayesian optimization to identify the best hyperparameters, enhancing the model's performance and accuracy.

  Model Evaluation: AutoML involves performing cross-validation and other evaluation techniques to assess model performance. It also includes metrics reporting, which provides comprehensive reports on various performance metrics such as accuracy, precision, recall, and more, ensuring a thorough understanding of the model's effectiveness.

  Model Deployment: AutoML simplifies the process of deploying machine learning models to production environments with minimal manual intervention. It ensures scalability, allowing deployed models to handle varying levels of demand by scaling resources up or down as needed. Additionally, it seamlessly integrates with other tools and platforms, facilitating easy implementation into existing workflows.

  Monitoring and Maintenance: AutoML involves continuous performance tracking to ensure models meet expected standards in the production environment. It includes drift detection to identify data drift and concept drift, signalling when the model's accuracy begins to degrade over time. The system provides alerts and notifications for anomalies or significant performance changes, enabling timely intervention. Additionally, it facilitates automated retraining of models using updated data to maintain their performance and relevance.

• Amazon SageMaker Autopilot is AWS's AutoML solution that automates the end-to-end process of machine learning model development. It starts with data exploration, identifying the machine learning problem, and selecting an appropriate algorithm based on the dataset and problem type. It also transforms the data to the format expected by the selected algorithm and performs training and hyperparameter tuning to find the optimal set of hyperparameters for the best-performing model. SageMaker Autopilot provides transparency by automatically generating and sharing feature engineering code. It also generates Jupyter notebooks that detail how the models were built, including data processing steps, algorithm selection, hyperparameters, and training configurations. This transparency helps users understand and trust the model development process. Users can customize certain aspects of the pipeline, such as selecting specific algorithms or defining custom preprocessing steps. SageMaker Autopilot seamlessly integrates with other AWS services like S3 for data storage, AWS Glue for data cataloguing, and SageMaker Studio for a comprehensive development environment.

• Users can interact with Amazon SageMaker Autopilot in several ways, such as programmatically through the SageMaker API, using the AWS CLI, AWS SDK, or the SageMaker Python SDK. Additionally, users can work with SageMaker Autopilot through SageMaker Studio, which is a workbench for end-to-end machine-learning activities. Regardless of whether you are interacting programmatically or using SageMaker Studio, you are using the same APIs.

Code to use SageMaker Autopilot programmatically:

import sagemaker
from sagemaker.automl.automl import AutoML

# Define the S3 path for the input data
input_data_uri = 's3://your-bucket/path/to/data.csv'

# Define the S3 path for the output data
output_data_uri = 's3://your-bucket/path/to/output/'

# Create a SageMaker session
sagemaker_session = sagemaker.Session()

# Create an AutoML job
automl = AutoML(role='your-sagemaker-role', 
                target_attribute_name='target_column', 
                sagemaker_session=sagemaker_session,
                max_candidates=10,  # Number of models to train and evaluate
                output_path=output_data_uri)

# Start the AutoML job
automl.fit(inputs=input_data_uri)

# Deploy the best model
best_candidate = automl.describe_auto_ml_job()['BestCandidate']
model = sagemaker.Model(model_data=best_candidate['InferenceContainerDefinitions'][0]['ModelDataUrl'], 
                        role='your-sagemaker-role', 
                        sagemaker_session=sagemaker_session)
predictor = model.deploy(initial_instance_count=1, instance_type='ml.m5.large')

• After completing an Amazon SageMaker Autopilot job, several resources and artefacts are automatically generated, including data transformation and configuration code, data exploration, and candidate generation notebooks. These notebooks provide visibility into the data exploration activities, detailing steps for each candidate model, including data preprocessors, algorithms, and hyperparameter settings. The generated artefacts help in refining models and are stored in the specified S3 bucket. Autopilot runs multiple experiments with various combinations of data transformation codes, algorithms, and hyperparameters to identify the best-performing model. Trained model artefacts and a leaderboard with metrics for each candidate are also stored in S3. Notebooks are accessible from both S3 and the SageMaker Studio console.
• Autopilot generates multiple model candidate pipelines which basically refers to an automatically generated sequence of steps that includes data preprocessing, feature engineering, model training, and hyperparameter tuning.

Model Hosting: Deploying ML model for consumption

• Autopilot in Amazon SageMaker helps identify the best-performing model for real-time predictions, which is crucial for handling customer feedback in e-commerce. By capturing real-time customer feedback from diverse channels such as emails, chat messages, calls, mobile app posts, social networks, and partner websites an e-commerce company can predict sentiment in real time. This enables swift responses to negative reviews, such as automatically engaging customer support or addressing product issues by removing problematic items from the catalogue. This proactive approach helps the business stay attuned to market trends and customer behaviour, allowing for timely interventions that can enhance customer satisfaction and prevent issues from escalating.
• Real-time prediction requires a model serving stack that includes your trained model and a hosting stack, typically involving a proxy or web server that interacts with your model serving code, allowing applications to make real-time API requests. SageMaker model hosting simplifies this process by managing instance types, and Docker containers for inference, while also supporting automatic scaling to effectively handle varying workloads.
• When using SageMaker model hosting, you choose the instance type and specify the count, along with selecting a Docker container image for inference. SageMaker then handles the creation and deployment of the model to the endpoint.

Deploying a candidate pipeline generated by Autopilot in Amazon SageMaker Inference pipeline:

• The inference pipeline allows you to host multiple models behind a single endpoint in SageMaker. This means you can deploy and manage multiple stages of your machine learning process (like data transformation, model inference, and post-processing) under one endpoint.

• Autopilot generates candidate pipelines combining feature engineering, algorithms, and hyperparameter configurations. The best-performing pipeline model can be deployed using SageMaker's inference pipeline feature, which allows hosting multiple stages (like data transformation, model inference, and post-processing) under one endpoint. However, the Inference pipeline has multiple containers. These include:

  Data transformation container will perform the same transformations on your dataset that were used for training. It ensures that your prediction request data is in the correct format for inference. This container processes and transforms the input data so that it matches the format expected by the model, allowing for accurate and reliable predictions.

  Algorithm container contains the trained model artefacts that were selected as the best-performing model based on your hyperparameter optimization tuning jobs.

  Inverse label transformer container is used to post-process predictions into a readable format that can be understood by the application consuming the model's output. It specifically converts numerical predictions, which may represent categories or labels in a machine-readable format (such as indices or one-hot encodings), back into their original non-numerical label values that are meaningful to humans.

• When an inference request is sent, data first undergoes preprocessing by the data transformation container to ensure it's in the correct format. The preprocessed data then flows sequentially through the remaining containers in the pipeline. Finally, the output of the last container in the pipeline (in this case, the inverse label transformer) is returned as the result to the client application that made the inference request.

Built-in Algorithms:

Benifits of using Built-in algorithms:

• Built-in algorithms require minimal coding experience. You can quickly start running experiments by simply providing the input dataset, setting hyperparameters, and specifying the compute resources (e.g., number and type of compute instances).
• They enable rapid prototyping and validation of ideas without the need for extensive development work. This accelerates the initial stages of machine learning projects.
• These algorithms are pre-tuned for common machine learning tasks, offering optimized performance out of the box. They are designed to efficiently handle large datasets and complex computations.
• Built-in algorithms are designed to leverage available hardware effectively, including support for GPUs and multi-instance scaling. This ensures efficient use of computational resources and faster processing times.
• They can scale seamlessly across multiple compute instances, which is particularly useful for handling large datasets and high-throughput requirements without additional configuration.
• Built-in algorithms are well-integrated with the broader ecosystem of machine learning platforms (such as AWS SageMaker). This ensures seamless workflows from data ingestion to model deployment and monitoring.
• These algorithms come pre-tuned for a variety of standard machine learning tasks such as classification, regression, clustering, and recommendation systems. This reduces the need for extensive hyperparameter tuning and custom configurations.
• Using built-in algorithms ensures a consistent and reliable approach to machine learning, as they are thoroughly tested and maintained by the platform providers.
• Built-in algorithms come with comprehensive documentation, examples, and community support, making it easier to troubleshoot issues and implement best practices.
• Machine learning platforms often ensure that built-in algorithms comply with security and regulatory standards, providing an additional layer of trust and safety for sensitive data and applications.
• By managing the underlying infrastructure and providing optimized performance, built-in algorithms can be more cost-effective than developing and maintaining custom algorithms.
• By handling the complexities of algorithm implementation and optimization, built-in algorithms allow data scientists and developers to focus more on business logic and the specific problem they are trying to solve.

When to Choose Built-in Algorithms:

• When you need to rapidly test and validate a machine learning concept.
• When you are addressing common problems like classification, regression, clustering, or recommendation systems.
• When your team lacks extensive machine learning expertise or coding skills.
• When you need to handle large datasets or require high-throughput processing.
• When you want to leverage optimized performance on available hardware, especially with large datasets.
• When your project has tight deadlines.

When to Choose Custom Code:

• If your problem requires specific algorithms or techniques not available in the built-in offerings.
• When you need to perform custom preprocessing, feature engineering, or use a novel model architecture.
• When you need fine-grained control over model performance and resource utilization.
• When your solution must deeply integrate with other custom software or hardware components.
• When you are conducting cutting-edge research or developing new machine learning methodologies.
• When your project is expected to evolve significantly over time, requiring ongoing adaptation and enhancement.

SageMaker Built-in Algorithms vs Script Mode vs Bring Your Own Container (BYOC):

Feature/Criteria	SageMaker Built-in Algorithms	SageMaker Script Mode	SageMaker Bring Your Own Container (BYOC)
Ease of Use	Very high - minimal setup required	Moderate - requires coding in supported frameworks	Low - requires Docker container setup and management
Customization	Low - limited to predefined algorithms	Moderate - custom training scripts allowed	Very high - full control over the environment
Supported Frameworks	Predefined algorithms in SageMaker	Popular ML frameworks (TensorFlow, PyTorch, MXNet)	Any framework that can run in a Docker container
Control Over Environment	Minimal control	Moderate control (training script)	Full control (entire Docker container)
Setup Time	Very quick	Moderate	High - need to build and manage Docker images
Scalability	Built-in scalability	Scalable with some configuration	Scalable but requires more setup and management
Performance Optimization	Pre-optimized for common tasks	Custom optimization possible through scripts	Fully customizable optimization
Dependency Management	Managed by SageMaker	Customizable via training script	Fully customizable within Dockerfile
Use Cases	Standard ML tasks like classification, regression, clustering	Custom ML tasks needing more control over training logic	Specialized tasks needing full environment customization
Skill Level Required	Beginner	Intermediate	Advanced
Example Algorithms	Linear Learner, XGBoost, BlazingText, etc.	Custom TensorFlow or PyTorch models	Any model, including those requiring special libraries
Deployment	Simplified via SageMaker interface	More involved but handled by SageMaker infrastructure	Most involved; user handles deployment using Docker
Integration	Seamless with SageMaker and other AWS services	Good integration with some manual setup	Full control over integration, requires manual setup
Use of GPUs	Supported where applicable	Supported, user must configure	Supported, user must configure
Automatic Scaling	Supported	Supported	Supported, but requires more configuration
Cost	Typically lower due to simplified management	Variable depending on custom setup	Potentially higher due to custom management and setup
Best for	Standard ML tasks with minimal customization needs.	Custom training logic with a need for more control over the training script while leveraging SageMaker’s infrastructure.	Highly specialized tasks requiring full control over the environment and dependencies.

Usecases and Algorithms:

Task	Use case	Built-in Algorithms
Classification	Predict if an item belongs to a category: an email spam filter	XGBoost, KNN
Regression	Predict numeric or continuous value: estimate the value of the house	Linear regression, XGBoost
Time series forecasting	Predict sales on a new product based on previous sales data	DeepAR forecasting
Dimensionality reduction	Drop weak features such as the color of the car when predicting its mileage	PCA
Anomaly detection	Detect abnormal behaviour	RCF (Random cut forest)
Clustering	Group high/medium/low spending customers from transaction details	Kmeans
Topic modeling	Organize a set of documents into topics based on words and phrases	LDA, NTM (Neural topic model)
Content moderation	Image classification	Full training, Transfer learning
Object detection	Detect people and object in an image	Object detection algorithm
Computer vision	Self driving cars identify objects in their path	Semantic segmentation
Machine translation	Convert spanish to english	Seq-to-Seq
Text summarization	Summarize a research paper	Seq-to-Seq
Speech to text	Transcribe call center conversations	Seq-to-Seq
Text classification	Classify reviews into categories	Blazing text classifier

Important links:

• Dataset
• AWS Data Wrangler
• AWS Glue
• AWS Athena
• Matplotlib
• Seaborn
• Pandas
• NumPy
• Measure Pretraining Bias - Amazon SageMaker
• SHAP
• Amazon SageMaker Autopilot
• Word2Vec algorithm
• AWS SDK for pandas
• usecases

Build Train and Deploy Ml pipeline using BERT:

Feature engineering:

• Feature engineering is the preocess of converting raw data from one or more resources into meaningful features that can be used for training machine learning models. You will apply your domain knowledge, business accuman and statistics to convert the features in to a features that can be readily useful by a machine learning algorithm that you chose to solve a specific problem.
• You perform feature engineering with two main goals in mind, first You are preparing your data so that it best fits the machine learning algorithm of your choice. Second by preparing ypur dat a you are trying to imporve the performance of the machine learning model.
• There are typically three steps involved in feature engineering: Feature selection, feature creation and feature transformation. All these steps may or may not be applicable to your specific usecase depending on the raw datset that you start with.

Feature engineering steps:

• Feature selection: Here you identify the appropriate data attributes or features to include in your training dataset as well as filter out any redundant and irrelevant features
• You perform feature selection with goal of reducing the feature dimentionality so that the reduced feature set can help train your model much more quickly.
• How do you select features to include in your dataset? ne of the technique to use is feature importance score that we have already learned about. Which will indicate how imp or relevant each one of the feature is to the final model as indicated by the importance score.
• Keep in mind that using feature importance score is only one of the technique that you can use to select appropriate features to include in your training dataset
• Feature creation Here you can combine existing features ti create new features or you can infer new attributes from existing attributes. In our usecase you can infer product review sentiment based on existing feature rating. The idea here is by creating and using these new features you need your machine learning model to produce more accurate predictions.
• Feature transformation This would involve calculating the missing feature values using a technique like imputation or scaling numerical feature values using techniques like standardization and normalization and finally converting non numerical features into numerical values so that the algorithm canmake sense of these non numerical features.
• It also involves categorical encoding ad embedding, normalization, standardization imutation and one hot encoding.

Next step: Pipeline

• Combined with the feature engineering steps along with the additional step to split the dataset into training and testing data you can built a feature engineering pipeline
• a generic feature engineering pipeline would look like
• 
• Start by selecting appropraiate features along with creating and selecting proper labels, The dataset is then balanced so that there is a correct representation from all classes of the labels this is followed by spliting the dataset into training validation and test dataset and finally you perform the transformation techniques that you have learned so for on these dataset.

Spliting of the dataset:

• Typically in machine learning you use a data set for both train the model as well as evaluate the model. Start by spolitting up the entire dataset into training data nad test data a major portion of training data is used to train your machine learning model. during the training process a small portion of the training data called validation data is used to frequently evaluate the model and tune the hyperparameters.Now the test data is the data that the model has never seen during the training threfore the test data is used to evaluate the trained model.
• Input of feature engineering pipeline is a raw data and the output is a set of features that are ready to be used for training the machine learning model.

Question:

• How do you take this generic pipeline and apply it to the specific usecase.

BERT algorithm and transforming raw product review text data into embeddings.

• BERT stands for bidirectional encoder representation from transformers which is a neural network-based technique for training NLP-based models

Difference between balzing text classifier and BERT

• Balzing text is based on word2vec whereas BERT is based on transformer architecture
• Both blazing text and BERT generate word embedings however blazing text operates at word level whereas BERT oprates at senetence level. Additionally using a bidirectional nature of the trnasformer architecture BERT can capture context of the word.
• Clarifiation: Blazing text learn word level embeddings for all the words that reincluded in training corpus. These vectors or embeddings are then projected into a high dimentional vector space. So similar words will generates a vectors that are close together and these are represented as very close to other in the learned vector space. The embedding that is generated by the blazing text for the word dress is close to regardless of the word dress word apears in the sentence the embedding generated for that particular sentence is alwas going to the same which means the blazing text is not really capturing the contex of the word in the senetnce.
• In contrast the input for BERT is not the word dress but the sentence itself. the output is once again the embedding but this time embdding is based on three indivicual components that is tokens segment and the position.
• For example lets say we have two sentences I love the dress. and I liove the dress but not the price. the context of the word dress is different in these two sentences Bert can take into considaration the words that come prior to the word dress as well as the words that follow the word dress.
• Using this bidirectional nature of the transformer architecture Bert is able to capture the context. SO the embeddings that are generated for the word dress in these two sentences will be completely different . However the length of these embeddings in these two sentences is going to be fixed. With BERT you encode sequences. Below show the end to end of converting a sequence into a bert embeddings that can be readily used by the BERT algorithm.

BERT EXample:

• The input is and input sequence. The input sequence could also consist of two different senetnces.The step is to apply the word piece tokenization; is a technique used to segment words into subwords and is based on pretrained model with dimensions of 768. Tokens are generated form the input sentence. In addition to the tokens coming from the individual words of the sentence. You will also see special token CLS. CLS specifies that this is a classification problem, If my input sequence consisted of multiple sentences then i would see another special token SEP that seperates the from the individual senetences.
• Once i have the word piece tokens the next step is to apply token embedding, to determine the token embedding from the individual tokens all i have tolook in to token in the 768 dimension vector that i mensioned before here the token CLS gets an embedding of 101. Thatis the position of CLS in that 768 dimension.
• Next tspe is to perform segment embedding.Segement emedding gets much more important when there are multiple sentencs in the input sequence. The segment ID 0 represnts that a sentence is a first sentence in the sequence and similarly the segmanet embedding of 1 respresents that it is a second senetnce in the input sequence.
• Here I have only one sentence in input sequence so i for all the individual tokens i get a segment embedding of 0.
• Next step is to apply position embedding, the position emebedding determines the index position of individual token in the input sequence. Here, my input sequence consists of four tokens. So based on a zero-based index, you can see the position embedding tokens for all the tokens.
• Once I have the three individual embeddings, it's time to pull all of these together. The final step includes determining an element wise sum of the position, segment and token embedding that have been previously determined.
• So the final embedding is of the dimension 1, 4, 768 in this particular case. And that makes sense because I started with one input sequence that consisted of three different words and I applied the Word Piece Tokenization that has pre-trained models of dimension 768.

Libraries and API to generate BERT embeddings from raw text programmatically:

• For this use the SCikit learn library, from the scikit learn you will use the RoBERTa tokenizer class. RoBERTa model is built on top of BERT model. It modifies few hyper parameter and the way the model is trained. It also uses a lot more training data than original bert model, this results in significant performnance improvement in variety of NLP task campared to the original BERT model.

COde to use RoBERTa model:

from transformers import RebertaTokenize
PRE_TRAINED_MODEL_NAME = 'roberta-base'

tokenizer = RobertaTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)

def convert_to_bert_input_ids(...):
    encode_plus = tokenizer.encode_plus(
    review, add_Special_tokens=True, max_length=128, return_token_type_ids=False, padding='max_length, return_attension_mask=True, return_tensors='pt', truncation=True'

return encode_plus['input_ids'].flatten().tolist()

• A brief note on the maximum sequence length parameter. This is a hyper parameter that is available on both BERT and RoBERTa models. The max of sequence length parameter specifies the maximum number of tokens that can be passed into BERT model with a single sample. To determine the right value for this hyper parameter, you can analyze your data set. Here you can see the word distribution of the product review data set. This analysis indicates that all of our reviews consist of 115 or less words. Now, it's not a 1-1 mapping between the word count and the input token count, but it can be a good indication. the real challenge comes in when you have to generate these embeddings at scale.
• The challenge is performing feature engineering at scale, and to address the challenge, you will use Amazon SageMaker processing. Amazon SageMaker processing allows you to perform data related tasks such as, preprocessing, postprocessing, and model evaluation at scale. SageMaker processing provides this capability by using a distributed cluster.

• By specifying some parameters, you can control how many notes and the type of the notes that make up the distributed cluster. Sagemaker processing job executes on the distributed cluster that you configure. Sagemaker processing provides a built-in container for Sklearn.
• Sagemaker processing expects data to be in S3 bucket. So you specify the S3 location where you're on, input data is stored and Sagemaker processing execute the Sklearn script on the raw data. Finally, the output, which consists of the embeddings, is persisted back into an S3 bucket.

Code to use SAgeMAker processing with sci-kit learn

from sagemaker.sklearn.processing import sklearnprocessor
from sagemaker.processing import processinginput, processingoutput

processor = SKLEarnprocessor(
             framework_version = 'SCIKIT_LEARN_VERSION, role = role, instance_type = 'ml.c5.4xlarge',instance_count=2')

processor.run(<parameters>)

code = 'preprocess-scikit-text-tobert.py'
inputs = [ProcessingInput(input_name = 'raw-input-data', source = raw_input_data_s3_uri, ... )]

outputs = [ProcessingOutput(output_name = 'bert-train', s3_upload_mode = 'EndOfJob', sources = s3 path)]

FEature store:

• A lot of effort goes into feature engineering. It would save you a lot of time, if you can store the results of feature engineering efforts, and reuse those results, so that you don't have to run the feature engineering pipeline again and again. It would save time not only for you, but for any other teams in your organization, that may want to use the same data and same features, for their own machine learning projects.
• Feature store, at a very high level, is a repository to store engineered features.
• For such a feature store, there are three high level characteristics that you want to consider. First, you want the feature store to be centralized, so that multiple teams can contribute their features to this centralized repository.
• Second, you want the features from the feature store to be reusable. This is to allow reuse of engineered features, not just across multiple phases of a single machine learning project, but across multiple machine learning projects.
• And finally, you want the feature store to be discoverable, so that any team member can come in and search for the features they want, and use the search results in their own machine learning projects.
• Now, if you extend the feature engineering pipeline that you reviewed before to include a feature store, it would look just like this.

• Besides the high level characteristics of being centralized, being reusable, and discoverable, other functional capabilities for a feature store include the ability to create the feature store itself, as well as the ability to create and ingest individual features, retrieve the features, as well as delete the features once they are obsolete.
• you can architect design and build such a feature store using mechanisms, like a database for persistence and APIs for creating, retrieving, and deleting the features. Or, you can use a AWS tool like Amazon Sagemaker Feature Store, which is a managed service that provides a purpose-built feature store.

Amazon SageMaker Feature Store

• Sagemaker Feature Store is a fully managed service that provides purpose-built feature store. SageMaker Feature Store provides you with a centralized repository to securely save and serve features from. SageMaker Feature Store provides you with the capabilities to reuse the features, not just across a single machine learning project, but across multiple projects.
• A typical challenge that data scientist see is training an inference skew that could result from discrepancies in the data used for training and the data used for inferencing. Sagemaker Feature Store helps reduce the skew by reusing the features across training and inference traces and by keeping the features consistent.
• SageMaker Feature Store provides the capabilities to query for the features both in real time and batch. The ability to creating for features in real time suppose use cases such as near real time ML predictions. Similarly, the ability to look up features in batch mode can be used to support use cases, such as model training.

To start using the Feature Store APIs:

from sagemaker.feature_store.feature_group import  FeatureGroup

reviews_feature_group_name = "reviews+distilbert+max_seq_length_128"
reviews_feature_group = FeatureGroupname=..., feature_definations=..., sagemaker_session=sagemaker_session(

reviews_feature_group.create(s3_uri = "s3 path".format(bucket,prefix), record_identifier_name=record_identifier_feature_name, event_time_feature_name=event_time_feature_name, role_arn=role)

# Ingesting features to feature group
reviwes_feature_group.ingest(data_frame=df_records, max_workers=3, wait=True)

# to retrieve features:
reviews_feature_store_query = reviews_feature_group.athena_query()

reviews_feature_store_table = reviews_store_query.table_name

query_string = 'SQL Query'.format(reviews_feature_store_table)

reviews_feature_store_query.run(query_string=..., ...)

• This results the queried features in a DataFrame format. Now, you have an option to convert that DataFrame into a CSV file and save the CSV file wherever you need to. In fact, you can store the CSV file into an S3 location and use that as a direct input to a training job on SageMaker.
• So far, you have seen the APIs to use with SageMaker feature store. If you'd like more of a visual approach, you can view the feature store and the featured groups created in a SageMaker studio environment.
• the feature definitions capture the featured name and the feature type. The SageMaker Studio environment also provides you with queries that you can use to interactively explore the feature groups. You can take the queries provided in this environment and run them in any query interface of your choice.
• you're storing the embeddings as one of the features and have the ability to creating the feature group to retrieve those input IDs.

Train and debug a custom machine learning model:

• For the fine tuning of the RoBERTa model is a supervised learning step. You will use the engineered features from the product reviews data, together with the sentiment label, as your training data. You will validate the model performance after each epoch using the validation data set. An epoch is a full pass through the training data set. In this step, you calculate the validation accuracy and the validation loss.
• Once you finish training your model, you will use the test data set the model hasn't seen before to calculate the final model metrics, such as test accuracy and test loss.

Difference between Built in algorithms and Pre-trained models

• In pre-trained model You will provide specific text data, the product reviews data, to adapt the model to your text domain and also provide your task and model training code. Telling the pretrained model to perform a text classification task, with the three sentiment classes

Model pretraining Model fine tunning:

• Pretraining is an unsupervised learning. In which it will only learn the vector representaion of the words.
• pretrained and all key models have been trained on large text corpus, such as large book collections or Wikipedia. In this unsupervised learning step, the model builds vocabulary of tokens, from the training data, and learns the vector representations. You can also pretrain NLP models on specific language data.
• There are many more pretrained models available, that focus on specific text domains and use cases:

Model fine tunning:

• Think of fine tunning as a transfer learning in NLP. It's a machine learning technique where a model is trained on one task and then repurposed on a second related task.
• For example, If you work with English product reviews as your training data, you can use an English language model, pretrained for example, on Wikipedia, and then fine tune it to the English product reviews.
• The assumption here is that the majority of words used in the product reviews have been learned already from the English Wikipedia. As part of the fine tuning step, you would also train the model on your specific NLP task. In the product reviews example, adding a text classifier layer to the pretrained model that classifies the reviews into positive, neutral, and negative sentiment classes.
• Fine tuning is generally faster than pretraining, as the model doesn't have to learn millions or billions of BERT vector representations. Also note that fine tuning is a supervised learning step, as you fit the model using labeled training data.

Where to find pretrained models:

• Many of the popular machine learning frameworks, such as PyTorch, TensorFlow, and Apache mxnet, have dedicated model huts, or zoos, where you can find pretrained models.
• The open source NLP project, Hugging Face, also provides an extensive model hub with over 8,000 pretrained NLP models. If you want to deploy pretrained models straight into your AWS account, you can use SageMaker JumpStart to get easy access to pretrained text and vision models.
• JumpStart works with PyTorch Hub and TensorFlow Hub and lets you deploy supported models in one click into the SageMaker model hosting environment. JumpStart provides access to over a 100 pretrained vision models, such as Inception V3, ResNet 18, and many more. JumpStart also lists over 30 pretrained text models from PyTorch Hub and TensorFlow Hub, including a variety of BERT models.
• In one click, you can deploy the pretrained model in your AWS account, or you can select the model and fine tune it to your data set. JumpStart also provides a collection of solutions for popular machine learning use cases, such as, for example, fraud detection in financial transactions, predictive maintenance, demand forecasting, churn prediction, and more.
• When you choose a solution, JumpStart provides a description of the solution and the launch button. There's no extra configuration needed. Solutions launch all of the resources necessary to run the solution, including training and model hosting instances.
• After launching the solution, JumpStart provides a link to a notebook that you can use to explore the solutions' features. If you don't find a suitable model via JumpStart, you can also pull in other pretrained models via custom code.

Implementing pretraining and fine tunnning with BERT models

• While you can use BERT as is without training from scratch, BERT uses word masking and next sentence prediction in parallel to learn and understand language. As BERT sees new text, the model masks 15 percent of the words in each sentence. BERT then predicts the masked words and corrects itself, meaning it updates the model weights when it predicts incorrectly. This step is called masked language model or masked LM.
• Masking forces the model to learn the surrounding words for each sentence. At the same time, BERT is masking and predicting words, or to be more precise, input tokens. It is also performing next sentence prediction, or NSP, on pairs of input sequences. To perform NSP, BERT randomly chooses 50 percent of the sentence pairs and replaces one of the two sentences with a random sentence from another part of the document.
• BERT then predicts if the two sentences are a valid sentence pair or not. BERT again will correct itself when it predicts incorrectly. Both of those training tasks are performed in parallel to create a single accuracy score for the combined training efforts. This results in a more robust model capable of performing word and sentence level predictive tasks.
• this pre-training step is implemented as unsupervised learning. The input data is large collections of unlabeled text.
• BERT has already been pre-trained on millions of public documents from Wikipedia and the Google Books corpus, the vocabulary and learned representations are indeed transferable to a large number of NLP and NLU tasks across a wide variety of domains.
• In the fine-tuning step, you also configure the model for the actual NLP task, such as question and answer, text classification, or a named entity recognition. Fine-tuning is implemented as supervised learning and no masking or next sentence prediction happens. As a result, fine-tuning is very fast and requires a relatively small number of samples or product reviews, in our case.
• For our usecase we will take the pre-trained RoBERTa model from the Hugging Face model hub and fine tune it to classify the product reviews into the three sentiment classes.

Train a custom model with Amazon Sagemaker:

• you learned how to train our product reviews text classifier using the building SageMaker blazing text algorithm. This time I show you how to train or fine tune the text classifier with a custom model code for the pre trained bert model you pull from the hugging face model hub. This option is also called bring your on script or a script mode in SageMaker.

• To start training a model in SageMaker, you create a training job. The training job includes The URL of the amazon S3 bucket where you have stored the training data. The compute resources that you want SageMaker to use for the model training. Compute resources are ml compute instances that are managed by SageMaker. The URL of the S3 Bucket where you want to store the output of the training job. The Amazon elastic container registry or Amazon ECR path, where the training code image is stored. SageMaker provides built in docker images that include deep learning framework libraries and other dependencies needed for model training and inference. Using script mode, you can leverage these pre built images for many popular frameworks, including TensorFlow, pyTorch, and Mxnet.
• After you create the training job, SageMaker launches the ml compute instances and uses the training code and the training data set to train the model. It saves the resulting model artifacts and other outputs in the S3 bucket you specify for the purpose.

steps you need to perform:

• 1. first you need to configure the training validation and test data set
• 2. You also need to specify which evaluation metrics to capture, for example the validation loss and validation accuracy.
• 3. Next you need to configure the model type of parameters such as number of epochs, learning rate etc.
• 4. Then you need to write and provide the custom model training script used to fit the model.

1. data set and the evaluation metrics:

• You can use SageMaker training input class to configure a data input flow for the training. Below is the code to configure training input objects to use the training validation and test data splits uploaded to an S3 bucket.

from sagemaker.inputs import TrainingInput

s3_input_train_data = TrainingInput(s3_data=path)
s3_input_validation_data = TrainingInput(s3_data=path)
s3_input_test_data = TrainingInput(s3_data=path)

• Define regex expression to capture the values of these metrics from the Amazon cloudwatch logs.

metric_definitions = [
{'Name': 'validation:loss', 'Regex':'val_loss([0-9\\.]+)'}, {'Name': 'validation:accuracy', 'Regex':'val_Acc: ([0-9\\.]+)'}]

2. Configure the model Hyperparametrs:

• Model hyper parameters include, for example, number of epochs, the learning rate, batch sizes for training, and validation data, and more. One important type of parameter for bert models is the maximum sequence length. the maximum sequence length refers to the maximum number of input tokens you can pass to the bert model per sample. I choose the value of 128 because the word distribution of the reviews showed that one 100% of the reviews in the training data said have 115 words or less.

hyperparametrs = {'epochs':3,'learning_rate':2e-5, 'training_batch_size': 256, 'train_steps_per_epochs': 50, 'validation_batch_size': 256, 'validation_steps_per_epochs':50, 'max_seq_length': 128}

3. Provide your custom training script:

• First you import the hugging phase, transform a library. Hugging face provides pretrained RobertaModel for sequence classification that already pre configured roberta for tax classification tasks, let's download the model conflict for this RobertaNodel. You can do this by calling RobertaConfig from pre-trained and simply provide the model name in this example, roberta-base. You can then customize the configuration by specifying the number of labels for the classifier. You can set non-labels to three representing the three sentiment classes. The ID to label and label to ID parameters. Let you map the zero based index to the actual class, label of -1 for the negative class. The label of 0 for the neutral class and the label of 1 for the positive class. You then download the pretrained RobertaModel from the hugging face library with the command. RobertaForSequenceClassification.

from transformers import RobbertaModel, RobertaConfig
from transformers import RobertaForSequenceClassification

config = RobertaConfig.from_pretrained('roberta-base', num_labels=3, id2label={0: -1, 1:0, 2:1},
label2id={-1:0,0:1,1:2})

model = RobertaFOrSequenceClassification.from_pretrained('roberta-base',config = config)
model = train_model(model, ...)

• With a pre-trained model at hand, you need to write the code to fine-tune the model here called train model. Below is the code to fine tune the model using pytorch

def train_model(model, train_data_loader, df_train, val_data_loader, def_val, args):
     loss_function = nn.CrossEntropyLoss()
     Optimizer = optim.Adam(param=model.parameters(), lr = args.learning_rate)
- Then you write the training code:
```python
for epoch in range(args.epochs):
    print('EPOCH -- {}'.format(epoch))
    for i, (sent, label) in enumerate(train_data_loader):
        if i< args.train_steps_per_epoch:
            model.train()
            optimizer.zero_grad()
            sent = sent.squeeze(0)
            output = model(sent)[0]
            _, predicted = torch.max(output, label)
            loss.backword()
            optimizer.step()
            
return model

4. FIt the model

from sagemaker.pytorhc import PyTorch as PyTorchEstimator
estimator = PyTorchEstimator(
    entry_point = 'train.py',
    source_dir = 'src',
    role = role,
    instance_count=1,
    instance_type = 'ml.c5.9xlarge',
    framewor_version = <PYTORCH_VERSION>,
    hyperparameters = hyperparameters,
    metric_definitions = metric_definitions)
    
estimator.fit(...) # To start the fine tunning of the model

DEbugging and profiling

• Training machine learning models is difficult and often a OPEC process and especially training deep learning models usually takes a long time with several training iterations and different combinations of hyper parameters before your model yields the desired accuracy.
• system resources could be inefficiently used, making the model training expensive and compute intensive.
• Debugging and profiling your model training gives you visibility and control to quickly troubleshoot and take corrective measures if needed. For example, capturing metrics in real time during training can help you to detect common training errors such as the gradient values becoming too large or too small. Common training errors include vanishing or explode ingredients.
• Deep neural networks typically learn through back propagation, in which the models losses trace back through the network. The neurons weights are modified in order to minimize the loss. If the network is too deep, however, the learning algorithm can spend its whole lost touch it on the top layers and waits in the lower layers, never get updated. That's the vanishing gradient problem.
• In return, the learning algorithm might trace a series of errors to the same neuron resulting in a large modification to that neurons wade that it imbalances the network. That's the exploding gradient problem. Another common error is bad initialization. Initialization assigns random values to the model parameters. If all parameters have the same initial value, they received the same gradient and the model is unable to learn.
• Initializing parameters with values that are too small or too large may lead to vanishing or exploding gradients again. And then overfitting, the training loop consists of training and validation. If the model's performance improves on a training set but not on a validation data set, it's a clear indication that the model is overfitting. If the model's performance initially improves on the validation set but then begins to fall off, training needs to stop to prevent the overfitting.
• All these issues impact your model's learning process. Debugging them is usually hard and even harder when you run distributed training. Another area you want to track is the system resource utilization monitoring and profiling. System resources can help you answer how many GPU, CPU, network and memory resources your model training consumes more. Specifically, it helps you to detect and alert you on bottlenecks so you can quickly take corrective actions.
• These could include I/O bottlenecks when loading your data. CPU or memory bottlenecks when processing the data and GPU bottlenecks or maybe underutilization during model training.

• If you encounter any model training errors or a system bottlenecks, you want to be informed, so you can take corrective actions. For example, why not stop the model training as soon as the model starts overfitting. This can help save both time and money and it's not only about stopping the model training when an issue is found, maybe you also want to send a notification via email or via text message in that case.

Implementation of debugging and profiling

• SageMaker Debugger automatically captures real time metrics during the model training such as training and validation loss and accuracy, confusion matrices and learn ingredients to help you improve model accuracy. The metrics from Debugger can also be visualized in SageMaker Studio for easy understanding.
• Debugger can also generate warnings and remediation advice when common training problems are detected. Also Debugger automatically monitors and profiles your system resources such as CPU, GPU, network and memory in real time. And provides recommendations on reallocation of these resources.
• This enables you to use your resources more efficiently during the model training and helps to reduce costs and resources.
• Debugger captures real time debugging data during the model training and stores this data in your security S3 Bucket. The captured data includes system metrics, framework metrics, and output tensors. System metrics include for example hardware resource utilization data such as CPU, GPU, and memory utilization. Network metrics as well as data input and output or I/O metrics. Framework metrics could include convolutional operations in the forward pass, batch normalization operations in backward pass. And a lot of operations between steps and gradient descent algorithm operations to calculate and update the loss function.
• And finally the output tensors. Output tensors are collections of model parameters that are continuously updated during the back propagation and optimization process, of training machine learning and deep learning models. The captured data for output tensors includes scalar values such as for accuracy and loss, and matrices for example representing weights, gradients, input layers and output layers.
• Now while the model training is still in progress, Debugger also reads the data from the S3 bucket and already runs a real time continuous analysis through rules. list of Debugger building rules you can choose from.
• You can also take corrective actions. In case Debugger detects an issue, for example, the model starts to over fit. You can use Amazon CloudWatch events to create a trigger to send you a text message, email you the status or even stop the training job. You can also analyze the data in your notebook environment using the Debugger SDK. Or you can visualize the training metrics and system resources using the SageMaker Studio IDE.

Code to leverage the building rules to watch for common training errors.

from sagemaker.debugger import Rule, rule_configs
rules = [
    Rule.sagemaker(rule_config.loss_not_decreasing(),
    Rule.sagemaker(rule_config.overtraining()]

from sagemaker.pytorch import PyTorch as PyTorchEstimator
estimator = PyTorchEstimator(
    entry_point = 'train.py',
    ...,
    rules = rules
    )
    
# Profile system and framework matrics for your training jobs
from sagemaker.debugger import ProfileRule, rule_configs
rules = [
    ProfileRule.sagemaker(rule_config.LowGPUUtilization()),
    ProfileRule.sagemaker(rule_config.ProfileerReport(),
    ...,
    ]
    
# COnfig profiler
from sagemaker.debugger import ProfileConfig, FrameworkProfile
profiler_config = ProfilerConfig(
    system_monitor_interval_millis=500,
    framework_profile_params = FrameworkProfile(num_steps =10))
    
from sagemaker.pytorch import PyTorch as PyTorchEstimator
estimator = PyTorchEstimator(
    entry_point = 'train.py', ...,
    rules = rules,
    profiler_config = profiler_config)

• Select the rules you want to evaluate, such as loss_not_decreasing or model starts to over train.
• Then pass the rules with the rules parameter in your estimator. SageMaker will then start a separate processing job for each rule you specify in parallel to your training job.The processing job will collect the relevant data and observe the metrics.
• To profile the system and framework metrics for your training jobs, you need to perform very similar steps.First you select the rules to observe again. Debugger comes with a list of building rules you can select such as check for low GPU utilization. If you select the ProfilerReport rule, the rule will invoke all of the building rules for monitoring and profiling. By default, Debugger collects system metrics every 500 milliseconds. And basic output tensors that is scalar outputs such as loss and accuracy every 500 steps. you can modify the configuration if needed.To enable the framework profiling, configure the framework profile params parameter Then pass the rules and profiler_config in the estimator as shown earlier.
• Note that the list of selected rules can contain both the debugging rules together with the profiling rules.

how can you analyze the results?

• For any SageMaker training job, the Debugger profiler report rule invokes all of the monitoring and profiling rules and aggregates the rule analysis into a comprehensive report.You can download the Debugger profiling report while you're training job is running or after the job has finished from S3.
• At the top of the report Debugger provides a summary of your training job.

• In rule summary section, Debugger aggregates all of the real evaluation results, analysis, rule descriptions and the suggestions.

• The report also shows system resource utilization such as CPU and network utilization over time.

• Debugger also creates a system utilization heat map.

• In the sample shown here, I have run the training job on a MLC59 X large instance which consists of 36 VCPU's. The heat map here shows how each VCPU was utilized over time. The darker the color, the higher the utilization. If you see resources being underutilized, you could scale down to use a smaller instance type, and save cost and run the training job more efficiently

MLOps overview:

• Automated pipelines actually span all of the workflow steps, including ingest and analyze, prepare and transform, train and tune, and finally deploy and manage.
• How does the broader concept of MLOps relate to building automated machine learning pipelines? MLOps builds on DevOps practices that encompass people, process, and technology.
• However, MLOps also includes considerations and practices that are really unique to machine learning workloads. All of these practices aim to be able to deliver machine learning workloads quickly to production while still maintaining high quality consistency and ensuring end-to-end traceability.

key considerations in ensuring your models have a path to production(difference between SLDC and MLDC)

• It's important to consider that the machine learning development life cycle is very different than the software development life cycle for a variety of reasons.
• First, the model development life cycle is difficult to plan for from a project management perspective. It typically includes longer experimentation cycles than you would see in a standard agile software development process.
• Also the development of machine learning models includes data tasks like feature engineering and data preparation. ou also have data processing code, as well as new inputs and artifacts to consider for versioning. You also have additional pipeline task as well.
• When you start to look at automating the machine learning workflow, the inputs and artifacts that are generated across these tasks result in multiple disparate pipelines with dependencies that can be a bit more challenging, stitched together than a typical software development workflow.
• Second, some models exist by themselves where you might be manually reading prediction requests and getting responses through a batch process or even within your notebook on an ad hoc basis. This is especially true in research environments.
• However, in many cases, a model is typically a small part of an overall solution that incorporates machine-learning. While that model is still a very key component to that solution, most often there is a need for other components that need to be built or integrated.
• As an example, consider your product review use case and your model that is predicting the classes of sentiment for a product review. That model itself will be able to classify the sentiment related to a product, but you also need to consider how that prediction will actually be used and potentially integrated into other existing applications.
• For this, there may be additional tasks like creating a rest API as a common interface for other applications to integrate with your model or even building applications that can respond to those reviews. This could mean creating automation to initiate back-end processes that allow for customer support engineers to quickly react and respond to any negative reviews.
• This brings me to the third consideration where typically multiple personas span the machine learning development lifecycle, and all are really needed to ultimately be able to build, deploy, integrate, and operate a machine learning workload.
• This can create challenges as these personas often have competing priorities and needs. There may also be skill gaps in building an operating machine learning workloads.
• As an example, a data scientist may not have a traditional IT background. While they may be very comfortable in creating a model that meets the performance objectives that have been identified for your particular machine learning use case, they may not know how to host that model in a way that it can be consumed by other applications or other systems.
• In this case, there may be a need to have a deployment engineer that is also engaged to help in building out the infrastructure and the resources that are needed to operate and host that model.
• Also, I mentioned that you might need to integrate that hosted model with another application. In this case, you're likely to depend on a software engineer to perform that integration. If there isn't a cross-functional team with the same project goals in place, competing priorities and skill gaps across these personas make it really difficult to provide that path to production for your model.
• Finally, many teams have processes in place supporting different regulatory or even internal corporate requirements. This means that when you're creating your machine learning pipeline, sometimes you also need to be able to ensure that traditional practices can be included inside the steps of your pipeline.
• Something like change management as an example here. This may mean that within your pipeline, you're going to automatically open a change ticket anytime a new model gets deployed to production. Or maybe it's a manual approval that's required before your model can deploy to production.
• All of these processes may need to be incorporated inside your machine learning pipeline.
• Considering and understanding all of these different aspects are key in ensuring you're able to provide a path to production of your model.
• I just covered the considerations for providing a path to production for your machine learning workloads and some of the common challenges that teams run into.
• MLOps aims to provide that path to production by reducing manual hand-offs between the steps in your workflow, increasing automation within those steps in your workflow, and then going a step further to orchestrate the steps across your workflow.
• But you don't want to just apply automation, you also want to improve the quality of your models. To do that, you need to establish mechanisms and quality gates inside your machine learning pipeline.

Workflow tasks without MLOps:

• Here's an example of a workflow starting with data ingestion and analysis. Here, a data engineer may create a raw dataset and manually send it to a data scientist.
• Then a data scientist is typically going to iterate through their model-building activities. This includes performing feature engineering, and data transformations, as well as experimenting with multiple hyperparameters and algorithms across their different experiments as they run through their model training and tuning activities as well. They typically iterate through these activities until they have a candidate model that is performing well according to their evaluation metric.
• At that point, a data scientist may hand that model off to a deployment team or an ML engineer who's responsible for deploying that model. If there's been limited communication between these teams up until this point in time, I often see this part result in a lot of delays because that model is essentially a black box to that deployment engineer.
• This means there's very limited visibility into how that model was built, how you would consume that model, and then how you monitor that model.
• To add to that, traditional deployment teams may not have a lot of experience in deploying and operating machine learning workloads. Once the deployment engineer has deployed the model, a software engineer often needs to create or make changes to applications that are going to then use that model.
• Finally, someone ultimately needs to operate that model in production. This typically means ensuring the right level of monitoring is set up, which can be challenging as the team that's operating the model may not be familiar with machine learning workloads or how to monitor a model. This can also include things like identifying and setting up the model retraining strategy as well.
• As you can see here, having a disconnected workflow with multiple manual hand-offs and limited cross team collaboration could really slow down your ability to get a model to production quickly and the ability to continue to iterate on that model as needed.

Let's now take a look at a view that incorporates cross team collaboration with automation to reduce those hand-offs and delays in a workflow.

• You can see in this view that you also incorporate a centralized Model Registry. the Model Registry holds key metadata and information about how your model was built, how the model performed with evaluation metrics. It's no longer a manual hand-off of a black box model. A Model Registry can also be used to trigger downstream deployment workflows as well.
• Once that model has been registered and approved, it can then trigger that downstream automated pipeline to deploy your model. That deployment pipeline typically includes the steps that are needed to package your model and then deploy it to one or more environments. Depending on the use case, that deployment may require additional code or packaging for consumption.
• Often a software engineer will be needed here to provide the steps and the code that is needed to create or update the API that will then be used to create a RESTful interface or a commonly defined interface for integrating with other applications.
• Finally, for operating your model, the correct monitors need to be identified and put in place early through your cross-functional teams. These monitors include traditional systems or performance monitors, but they also include model specific monitors like checking for things like model quality drift or data drift.
• As you can see here, visibility into those monitors is provided back to the personas that are involved in creating that end-to-end workflow.
• This is because there are some aspects of that monitoring that different personas may need more visibility into. As an example here, some logs may be generated by model monitors that the data scientist or machine learning engineers want to have visibility into
• In this case it's key to make sure that you're providing that back as a feedback mechanism and providing that transparency and visibility to those personas.

• I've talked about automation within each of these workload steps, but how do you coordinate performing the steps across your workflow? This brings us to orchestration.

Orchestration vs Automation

• With automation, you're typically looking at automating the step and the tasks within that step, that are required to accept an input or inputs and then ultimately produce some type of output or artifact.
• As an example, your data preparation step may include one or more tasks that can help you automate and produce the intended output. In this example, you have your raw dataset input, and that's automatically ingested by the data processing step that is then responsible for taking that raw data, and transforming it into the format that can be consumed by your algorithm.
• This step then produces an output artifact. In this case, it's your transformed data that can then be used and consumed by the next step in your pipeline. This would be your training and your validation datasets. It's important to not only automate these individual tasks, but also the end-to-end machine learning workflow as well.
• To automate the steps across your end-to-end workflow, you also need to add a layer that can provide overall orchestration, in defining when and how these individual steps with automated task are run.
• Automation is great for reducing cycle time and deploying more quickly, but what about improving and ensuring the quality of your model? Your orchestration layer can also provide those quality gates that I talked about a bit before.
• You can use this orchestration layer to implement quality gates between your steps to determine when the pipeline should proceed to the next step in your machine learning pipeline.

• Quality gates The term quality gates refers to having an automated, or manual checkpoint within your pipeline that gates whether the next step in your pipeline can run based on some type of criteria or a conditional step that you define.
• types of quality gates that can be used and included inside your machine learning pipelines As an example for data curation, you may have a quality gate that governs and restricts access to the data that can be used. For model building, you're typically setting up a minimum threshold for your model evaluation metrics. This typically means establishing a metric that you're trying to optimize for, so something like accuracy or F1 score.
• Then establishing the lower boundary for that metric as a gate to proceed to the next step in your deployment pipeline. For model deployment, you can use something like A/B deployment strategy where you have model version 1 that's deployed into production, and you want to slowly deploy model version 2 to serve a portion of the traffic that's reaching your model.
• You can then evaluate how model version 2 is performing relative to model version 1. If model version 2 is performing better than model version 1, you can then start to shift more traffic to that model version 2. For model integration, you want to make sure that the application that's consuming your model is able to get prediction requests bag
• In these tests you're often making sure to check for things like your inference code is synchronized with your model and potentially the API. Then finally for model monitoring, you want to monitor for standard metrics, so things like CPU, GPU, or memory utilization. However, you also want to set up those monitors that are monitoring specifically for your model.
• Things like data drift, which can indicate that the data that you used to train your model now looks much different than the actual ground truth data. In this case, you want to set up the monitor with alerts so that you can get notified of any potential issues with model performance. This allows you to take action such as start of retraining pipeline.
• there is a lot to consider when operationalizing your machine learning workloads and applying MLOps practices like, creating machine learning pipelines.

Creating a Machine learning pipelines:

• We will cover creating machine learning pipelines as a way to iterate quickly, with reduced handoffs and reduce manual effort. quality gates into your pipeline so that you can not only iterate quickly, but also iterate with improved quality and traceability.
• If you look at the high level tasks in your machine learning workflow, when you create a pipeline, you aim to automate the task and orchestrate the sequence and conditions of running that task. You also need to consider your pipeline triggers.
• In the case of software development life cycles(SDLC), it's usually pretty clear when you have a commit to a source code repository, you're going to automatically start an automated bill. However in the case of a machine learning pipeline you have multiple potential triggers. Such as a change to algorithm, hyper parameters or code, or alternatively having new training data, which could also be another potential trigger for your machine learning pipeline.
• Another key goal in building out an effective pipeline is establishing traceability, so ideally you have a central view of how pipeline artifacts were built. This is important not only for visibility and support, but also in the event of needing to recover any resource or artifact that's part of your pipeline.
• Let's take a closer look at each of these tasks and the considerations for each.
• Data task or specifically Data ingestion for model development: I work with a lot of teams. Where in this view you reach out to your data engineer, you ask them for some data, sometimes you need to go through a series of security approvals as well. And then ultimately get a data set that you can use for your model building activities.
• However there are challenges with this model as you can imagine. First, it can really slow down model development time. Second, it can also result in limited traceability with these manual handoffs of data. Third, it also makes model development difficult in terms of automating any kind of retraining workflows.While you may be able to do some of those initial model build activities, retraining pipelines in this particular context are very difficult to support.
• It's generally recommended to establish a data lake that provides governed access to data scientists or automated processes. This allows data scientists to quickly start a model development activities, and it also ensures there's traceability for that data because you know which data scientists have checked out specific data sets. This also allows you to create a model retraining workflow as well where the consumer in this particular case is the actual deployment pipeline or the machine learning pipeline, as opposed to the data scientist.

• data pre-processing and feature engineering After you get the data, a typical machine learning pipeline starts with the task that's needed to then take that raw data. And then transform it into a format that the algorithm can understand and that you can use for your model training or building activities. As we've discussed in the previous session's, your data pre-processing and feature engineering, can include any process or set of tasks. Whether it's a Python script or even another model that's being used to transform your data into the features, that will ultimately be used for training your model.
• When you automate your data processing and feature engineering, you have a key input and that's your raw data. And this typically gets fed in through automation, and then that automation will extract and transform those features, ultimately producing artifacts that will be used in training. The artifacts that are produced in your data preparation step include your training, validation, and test data sets.

• traceability being a benefit of machine learning pipelines. Traceability is partially a result of the versioning of the inputs and the outputs for each workflow step. For your data task, you have code versioning for the code that you used to transform the data, but you also have data versioning.
• So as you can see in this slide, an example here would be that maybe you have that raw data set on input that's version one, and then you perform some feature transformations to ultimately create your output artifacts. So those output artifacts in this case, have aversion associated with them as well. So in this case your training validation and test data sets are your artifacts and maybe be at version 13. And this could be because you've had multiple iterations of feature engineering and data transformations, until you got it into the format that you really want it to be. The key here is that all of your inputs and artifacts that are produced as part of an automated step, should have versions associated with them.

• Finally for your data task, there's a number of validation tests that could also be incorporated into your machine learning pipeline and automated to improve quality.
• A few examples here include data quality checks where you can implement automated steps that gather statistics about your input data to look for any signals of potential data quality issues. So things like a specific attribute having an unexpectedly large number of missing values on input.
• Another example here would be checking for indicators of statistical bias. Finally data schema is something that you can include in your automated checks as well. So in this case you can embed a quality check inside your pipeline that ensures that the data structure and schema that's coming in on input, is in the format is expected to be.
• The key here is not only performing these tasks but also automating them as part of your machine learning pipeline.
• Training model the output of your previous task, then becomes the input into the next task. So in this case the output was your training validation and test data sets, which will be used in training and tuning your model as well as evaluating your model.
• The output includes a number of potential candidates until you find the best performing model according to your evaluation criteria. it will typically include evaluation metrics such as training metrics and model validation metrics at a foundational level.

• Model deployment task For a model deployment task, you're taking that model artifact and you're deploying it for consumption. This can take two different forms, you can either deploy in batch mode where you're basically wanting to send in batch records for prediction, and then receive batch responses back.
• Or you can deploy your model for a real-time or persistent endpont. An endpoint can consistently serve prediction requests and responses through a serving stack. And the serving stack typically includes a proxy, a web server, that can then accept and respond to your requests coming in on input.

• When you're setting up your model deployment task as part of your machine learning pipeline. It's important to understand how your model will be consumed when you're setting up that model deployment task as part of your pipeline. Because as you can see here, the surrounding resources that need to be built, will differ between these two different forms.
• Operating task Finally once you've deployed and likely integrated your model into a broader solution, you have your operating task to consider. The output of your previous set of tasks is a deployed model or models that are then available for consumption.
• Although operating tasks are at the end of our workflow here, when you're setting up your pipeline you really need to be considering your operating tasks early so that they can be incorporated into your workload early.
• As an example if there's runtime data that you need to capture, you need to ensure that the code is in place to capture and log that data. As an example here you could be capturing the request for predictions that come in, as well as the prediction responses that get returned.
• In this case for your product review use case, you may have a product review coming in that's entered through, say a web page and it ultimately gets classified as either negative, neutral or positive. You may want to capture that prediction request coming in, but you may also want to capture that prediction response going back out. And store it in a secondary data store to be able to perform additional reporting or analysis like determining whether a specific vendor supplying your product has potential quality issues.
• Also your operating tests include setting up monitors and alerts so that you can ensure that your deployed model has ongoing monitors that look for signs of things like model degradation. And also monitor the health of the systems that are supporting your model. So looking for common system metrics like CPU utilization or GPU utilization, that are supporting your machine learning model.

• One common challenge I run into is being able to have central visibility into your machine learning workflow, across all of the different personas and roles that are typically involved in an end to end machine learning workflow.
• This is a challenge whether it's having visibility into the status of your pipeline, so knowing when a specific version of a model is deployed to production. Or having visibility into system performance for debugging or even having visibility into model performance to see how your model is performing over time.
• In this case, dashboards can serve as a central feedback mechanism for your machine learning pipelines as well as your deployed models.

Model Orchestration

• I've talked about the steps in creating your machine learning pipelines and the tests that can be automated within those steps, but how do you bring it all together into an end to m pipeline? This is where pipeline orchestration comes in because you need a way to not only automate the tasks within your steps, but you also need a way to coordinate those steps that are performed across your end to end workflow.
• There are a lot of great choices for pipeline orchestration but orchestration essentially allows you to manage the end to end traceability, of your pipeline. But focusing on using automation to capture the specific inputs and outputs of a given task, and the artifacts that are then produced by those tasks, and then bringing them all together in a controlled end to end pipeline.

Model lineage and Artificate tracking

• Bringing all of your automated tasks and steps together in an end-to-end pipeline, allows you to trace the inputs that are used by each step in your pipeline, as well as the artifacts or outputs that are produced by each step in your pipeline.
• Model lineage essentially refers to understanding and tracking all of the inputs that were used to create a specific version of a model.
• There are typically many inputs that go into training a specific version of a model. These inputs include things like the version of the data that was used to train the model in combination with the versions of the code and the hyperparameters that were used to build the model. However, inputs also includes things like the versions of the algorithms or the frameworks that were used. Depending on how you're building your model, this can also include things like the version of your docker images that were used for training, as well as different versions of packages or libraries that were used. it's basically all of the data that tells us exactly how a specific version of a model was built.

• For an example; you can see all of the model inputs that were used to create version 26 of this model. Each of these inputs has a version or multiple versions associated with it. An input may even have some additional metadata as well.
• As an example, for your Python code, you probably have a commit hash for the source code commit that was used to commit this particular piece of code. But you may also want to capture additional metadata, like the name of the source code repository, so that all of these inputs together are the main data points that allow you to capture the information and provide a complete picture about how this model was actually built.
• You also typically want to capture information about the trained model artifact itself as well. Things like the evaluation metrics for that particular version, as well as the location of the model artifacts. As you can see, this is a lot of information to track
• Where does that information about model lineage get stored, and how do you capture all of this information as part of your machine learning workflow? This is where model registry comes in. A model registry is a central store for managing model metadata and model artifacts.

• When you incorporate a model registry into your automated pipeline, it can provide traceability and auditability for your models, allowing you to effectively manage models, especially when you begin to manage at scale and you have tens or hundreds or even thousands of models.
• A model registry also gives you the visibility into how each of the model versions was built. It also typically includes other metadata, such as information about environments where a particular version of a model is deployed into.
• Model registry is a centrally managed model metadata and model artifact tracks which models are deploy across environments
• Keep in mind though, that a model is one artifact that's produced as part of your machine learning pipelines. There's other outputs and artifacts that are produced that you also want to consider for complete end-to-end traceability.

an example of artifacts that are produced in your machine learning workflow and why artifact tracking is so important.

• Artifact is the output of a step or task that can be consumed by the next step in pipeline or deployed directly for consumption.
• In below you can see your machine learning workflow with corresponding tasks. Let's assume that these tasks have been automated, and you're now orchestrating these tasks into your machine learning pipeline.
• or each task, you have a consumable artifact that becomes the input into the next task. Each of these artifacts has different versions associated with them. For your data task, your process training dataset is an artifact from this task. In your model-building task, your model artifact that is produced becomes input into your model deployment task. A machine learning pipeline really provides a consistent mechanism to capture the metadata and the versions of the inputs that are consumed by each step, as well as the artifacts that are produced by each step.

But why is all this so important?

• operational efficiency is one key reason. When you need to go and debug something, it's important to know what version is deployed at any given time, as well as what versions of the inputs were used to create that deployable artifact or the consumable artifact.
• It's also important for the reliability of your workload. Because what if, for example, a human goes in and inadvertently deletes a live endpoint? Without knowing exactly how that endpoint was built, it's difficult, if not impossible, to recover that endpoint without disruption in your service.

How to create Machine learning pipeline with amazon sagemaker pipelines:

• Sagemaker Pipelines allows you to create automated workflows using a Python SDK, that's purpose-built for automating model-building tasks. You can also visualize your workflows inside Amazon SageMaker Studio. Pipelines also includes the ability to natively integrate with SageMaker Model Registry. This allows you to capture some of that Model Metadata that I previously discussed, like the location of your training model artifact in S3, or key information about your trained model, so things like model evaluation metrics. Model Registry also allows you to choose the best performing model that you want to approve for deployment. Finally, SageMaker Projects allows you to extend your pipelines, and incorporate CI/CD practices into your machine learning pipelines. This includes things like source and version control for that true end-to-end traceability.

Features or components of pipelines:

• Pipelines First, you have pipelines, which allows you to build automated Model Building workflows using your Python SDK. Again, these workflows can be visualized inside SageMaker Studio.
• SAgemaker Model registry Second, you have SageMaker Model Registry, which stores Metadata about the model and has built-in capabilities to include Model Deployment approval workflows as well.
• Sagemaker projects Finally, you have Projects which includes built-in project templates, as well as the ability to bring your own custom templates that establish and pre-configured a pattern for incorporating CI/CD practices into your Model Building Pipelines and your Model Deployment Pipelines.
• SageMaker Pipelines provides the ability to work with all three of these components.

Sagamker pipelines:

• SageMaker Pipelines allows you to take those machine learning workflow tasks, that I've been talking about and automate them together, into a pipeline that's built through code.
• A Python SDK is provided, so that you can build and configured these workflows. The pipeline visualizations again, which are similar to what you see here, are all provided through SageMaker Studio.
• Pipelines provides a server-less option for creating and managing automated machine learning pipelines. Meaning, you don't have to worry about any of the Infrastructure or managing any of the servers that are hosting the actual pipeline.

• if you look at the first step in your pipeline, you have a data processing step. For this, SageMaker pipelines supports Amazon SageMaker processing jobs that you can use to transform your raw data into your training datasets. SageMaker processing expect your input to be an Amazon S3. Your input in this case is your raw data, or more specifically, your product review dataset. SageMaker processing also expects your data processing script to be an S3 as well. in this case, your script is your Scikit-learn data processing script that will be run for your processing job, in that will be used to transform your data, and split it into your training test and validation datasets.

Code to configure this step in to pipeline:

# Define step Inputs and Outputs:
processing_inputs = [
    ProcessingInputs(
        input_name = 'customer-reviews-input-data',
        source = 's3 path',
        destination = 'path',
        s3_data_distribution_type = 'ShardedByS3Key'
    )
]

processing_outputs = [
    Processingoutputs(...)]
    
# Configure processing step:
processing_step = Processing_step(
    name = 'Processing',
    code = 'path', # Name of the script that you want to run
    processor = processor, # sklearn processor
    inputs = processing_inputs,
    outputs = processing_outputs,
    job_arguments = [ # 
        '--train-split-percentage',
        str(train_split_percentage.default_value,
        ...
    )])
    
    # Use amazon sagmaker training jobs to train the model using the oututs from the previous step as input
    # the output of your processing step is then fed into the input of your training step.
    #  In this case, you'll want to use your training dataset to train the model and then you're going to use the validation dataset to evaluate how well the model is actually learning during training
    # The output of this particular step is going to be a trained model artifact that gets stored in S3.
    
    # COnfiguring the hyperparameters that we'll use as input into the training job step.
hyperparameters = {
    'max-seq-length' : max_seq_length,
    'epochs' : epochs,
    'learning_rate' : learning_rate,
    ...
    }
    
    # Configure the Estimator:
from sagemaker.pytorch import pyTorch as PyTorchEstimator
estimator = PyTorchEstimator(
    entry_point = 'train.py',
    source_dir = 'src',
    role = role,
    instance_count = train_instance_count,
    instance_type = train_instance_type,
    volume_size = train_volume_size,
    py_version = 'py3',
    framework_version = '1.6.0',
    hyperparameters = hyperparameters,
    metric_definitions = metric_definitions,
    input_mode = input_mode
    )
    
# COnfigure the training step:
training_step = Training_step(
    name = 'Train',
    estimator = estimator,
    inputs={
        'train': TrainingInput
        s3_data = processing_step.properties.ProcessingOutputCOnfig.Outputs[
            'sentiment-train'
            ].S3Output.S2Uri,
            content_type = 'text/csv')
            , 'validation' : TrainingInput(...)
    })
    
# Use Amazon Sagemaker processing to evaluate trained model using test holdout dataset
# The trained model artifact then becomes input into the next step inside your pipeline, which is the evaluation step.

how do you then use it for model evaluation in your SageMaker processing job?

# Model evaluation code/script
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score

def predict_fn(input_data, model):
    ...
    return predicted_clsses_jsonlines
    
...
y_test = df_test_reviews['review_body'].map(predict)
y_actual = df_test_reviews['sentiment'].astype('int64')

print(classification_report(y_true = y_test, y_pred = y_actual))
accuracy = accuracy_score(y_true=y_test, y_pred=y_actual)
print('Test accuracy: ',accuracy)

# This evaluation script is provided as input to Sagemaker processing job for model evaluation
# Once the processing job has completed as a step inside your pipeline you can then analyze the results as shown below

from pprint import pprint
evaluation_jason = sagemaker.s3.S3Downloader.read_file(
    "{}/evaluation.json".format(evaluation_metrics_s3_uri))
    
print(json.loads(evaluation_json))

# {'metrics':{'accuracy': {'value': 0.74}}}

# how you include this step inside your pipeline; This step is going to look similar to the data processing test step that i previously shown but there is one exception PropertyFile

# DEfine Output
from sagemaker.workflow.properties import PropertyFile
evaluation_report = PropertyFile(
    name = 'EvaluationReport',
    output_name = 'metrics',
    path = 'evaluation.json'
    )
    
# In this case, the property file will include your evaluation metrics, which will then be used in a conditional step that determines whether or not you want to deploy this model based on the metrics in that file. 

# Configure the pipeline processing job 
evalustion_step = ProcessingStep(
    name = 'EvaluationModel',
    processor = evaluation_processor,
    code = 'src/evaluation_metrics.py',
    inputs=[
        ProcessingInput(...),
        ],
        outputs = [
        ProcessingOutput(...),
        ],
        job_arguments = [...],
        property_files = [evaluation_report],
        )
# Condition step: Use Amazon Sagemaker Pipeline condition step to conditionally execute step(s)
# COnfiguring condition step: Define a condition and import conditional workflow step:
min_accuracy_value = ParameterFloat(
    name = 'MinAccuracyValue',
    default_value = 0.01
    )

from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo

from sagemaker.workflow.condition_step import(ConditionsStep, JsonGet,
)

# COnfigure the condition
minimum_accuracy_condition = ConditionGreaterThanOrEqualTo(
    left = JsonGet(
        step = evaluation_step,
        property_file = evaluation_report,
        json_path = 'metrics.accuracy.value'),
        right=min_accuracy_value)
        
# OCnfigure this step 
minimum_accuracy_condition_step = ConditionStep(
    name = 'AccuracyCondition', 
    conditions = [minimum_accuracy_condition],
    # Success continue with model registration
    if_steps = [register_step, create_step],
    else_steps = [] # Fail end the pipeline
    )

• In this case, the condition is, if accuracy is above 99 percent, you will register that model and then create the model. Which essentially packages that model for deployment.

• Now that I've covered the pipeline steps that are used for your data and your model building or training tasks,

Registering the model and creating model package that can be used for deployment

• one of the key components of SageMaker Pipelines is SageMaker model registry. It's very difficult to manage machine learning models at scale without a model registry. It's often one of the first conversations that I have with teams that are looking to scale and manage their machine learning workloads more effectively.
• SageMaker model registry and incorporating it as a step inside our pipeline. SageMaker model registry contains a central catalog of models with their corresponding metadata. It also contains the ability to manage their approval status of a workflow by either marking a model as approved or rejected.
• Let's say the model accuracy is still lower than required for production deployment. As a machine learning engineer, you may want to mark that model as rejected so it's not a candidate for deployment to a higher-level environment. You could also use the model registry as a trigger for downstream deployment pipeline so that when you approve a model, it then automatically kicks off a deployment pipeline to deploy your model to downstream environments.
• When you register your model, you want to indicate which serving image should be used when you decide to deploy that model. This ensures that not only do you know how the model was trained through the metadata that you capture in the model registry, but you also know how you can host that same model because you've defined the image to use for inference.
• You also need to define your model metrics where you're essentially pulling data that already exists about your model, but ensuring that it's stored as metadata into that central model registry.
• Finally, you configure the actual step inside SageMaker Pipelines using the built-in function called Register Model.
• In your configuration, you include the container image that should be used for inference, the location of your model artifact in S3, the target configuration for the compute resources that you would use for deployment of the model, as well as some metrics that are very specific to this model version.
• All of this metadata will be used to populate the model registry. You can see here that when you register the model, the approval status is also a configuration parameter that you can optional use to set the approval status for the model when you register it. The default is to set the approval status to pending manual approval, which is more in line with the continuous delivery strategy versus the continuous deployment strategy because you're indicating that you still want a human to approve that model manually before you start any downstream deployment activities.

Code to configure model registry:

# Define deployment image fro inferance
inferance_image_uri = sagemaker.image_uris.retrieve(
    framework = 'pytorch',
    region = region,
    version = '1.6.0',
    py_version = 'py36',
    instance_type = deploy_instance_type,
    image_scope = 'inferance'
    )
    # Defininf model metrics to be stored as metadata:
    from sagemaker.model_metrics import MetricsSource, ModelMetrics
    
    model_metrics = ModelMetrics(model_statistics = MetricsSource(
        s3_uri = 's3://...'),
        content_type = 'application/json')

# COnfigure the model registry step:
register_step = RegisterModel(
    name = "RegisterModel",
    estimator = estimator,
    image_uri = ...,
    model_data = 
    training_step.properties.ModelArtifacts.S3ModelArtifacts,
    content_types = ["application/jasonlines"],
    response_types = ["application/jasonlines"],
    inferance_instances = [deploy_instance_type],
    transform_instances= ['ml.m5*large'] # batch transform
    model_package_group_name = model_package_group_name,
    approval_status = model_approval_status,
    model_metrics = model_metrics)

# How do you link all these steps together? Once you have all these steps configured within your pipeline, you now need to link them together to create an end-to-end machine learning pipeline. To link all of these steps together, you need to configure the pipeline using the pipeline function that's part of the SDK.
# Configure the pipeline:
from sagemaker.workflow.pipeline import Pipeline

pipeline = Pipeline(
    name = pipeline_name,
    parameters = [
        input_data,
        processing_instance_count,
        ...
        ],
        steps = [processing_step, training_step, evaluation_step, minimum_accuracy_condition_step], sagemaker_session = sess,
        )
        
# To actually run this pipeline Create and execute the pipeline:
response = pipeline.create(role_arn = role)

pipeline_arn = response['PipelineArn']

execution = Pipeline.start(
    parameters = dict(
        InputData = raw_input_data_s3_uri,
        ProcessingInstanceCount = 1,
        ...
        ))
        
# when you start your pipeline, you can then visualize the status of each of your steps through SageMaker Studio or you can describe the status of your steps using the Python SDK. 

Sagemaker Projects:

• Projects allows you to automatically incorporate CI/CD practices such as source control and setting up automated workflows to automatically initiate downstream deployment processes based on an approved model in your model registry.
• when you talk about creating machine learning pipelines, you focus first on automation including quality gates, tracking model lineage and some of the key artifacts that are produced as part of that pipeline.
• Incorporating additional CI/CD practices becomes more of an advanced topic. We're choosing not to dive too deep into that particular aspect in this particular session, because it's important to first understand the components of a machine learning pipeline.
• Including how to automate the steps in your workflow, as well as how to orchestrate those steps. You can then continue to evolve and improve your pipelines by incorporating CI/CD practices.
• SageMaker projects gives you a lot of those capabilities with preconfigured MLOps templates. SageMaker projects integrates directly with SageMaker pipelines and SageMaker model registry.
• And it's used to create MLOps solutions to orchestrate and manage your end to end machine learning pipelines, while also incorporating CI/CD practices.
• With projects you can create end to end machine learning pipelines that incorporate CI/CD practices like source and version control, as well as the ability to trigger downstream deployments off of an approved model in the model registry.
• Projects have built in MLOps templates that provision and pre configure the underlying resources that are needed to build end to end CI/CD pipelines. These pipelines include things like source control for your model build and deployment code, automatic integration with SageMaker model registry, as well as approval workflows to start downstream deployments to other environments.
• You can use these built-in project templates or you can also create your own project templates.

Course 3

Week1 Outline:

• Dive into a few advanced concepts in training and tuning machine learning models. When training machine learning models, hyper parameter tuning is a critical step to get the best quality model and this could mean a model with the highest possible accuracy or lowest possible error.
• Learn about a few popular algorithms that are used for automated model tuning and then I will discuss automated hyper parameter tuning on amazon Sage maker.
• Learn how to apply the automated hyper parameter tuning to BERT based NLP or the natural language processing text classifier.
• Hyper parameter tuning is typically a time consuming and a compute intensive process. Learn about the concept of warm start with hyper parameter tuning on Sage maker that allows you to speed up your tuning jobs.
• Introduce a concept of check pointing in machine learning and discuss how Sage maker leverages the idea of check pointing to save on training costs using a capability called managed spot training.
• Introduce tune distributed training strategies; Data parallelism, and model parallelism that allow you to handle training at scale. The challenges addressed by these strategies include training with large volumes of data as well as dealing with increased model complexity.
• Discussion of Sage maker capability of bringing your own container, which allows you to implement your own custom logic for your algorithms and train on Sage maker managed infrastructure.

Advanced Model Training and Tunning:

• Discuss training and tuning strategies that help with challenges related to skill of training and tuning machine learning models while optimizing machine learning costs.

• Introduce SageMaker capabilities for automated hyperparameter tuning, distributor training, and optimizing training costs. Discussion on how you can use your custom algorithms with SageMaker.

• Model tuning is a part of model training process. The goal of model training process is to fit the model to the underlying data patterns in your training data and learn the best possible parameters for your model. Some example parameters include node rates and biases.

• For example, the roberta-base model that you have used in the previous courses of this specialization comes with 125 million parameters.

• In contrast, model hyperparameters need to be defined before the training process starts because these hyperparameters influence the model learning process. Some hyperparameters include number of epochs, the learning rate, as well as the batch sizes to use during your training.

• You evaluate the model's performance continuously during your model training process to find the model accuracy using a holdout validation dataset. During this process, you fine-tune the model parameters and hyperparameters as necessary. "If you can't measure it, you can't improve it."

• An important step in model development is to evaluate the final model with another holdout dataset called test dataset that your model has never seen before. These final model metrics can be used to compare and contrast competing models. Typically, the higher this final score is, the better is the ability of the model to generalize.

• Hyperparameter tuning is an important part of model development process. When you start working on a new model, you're most likely to start with manually selecting hyperparameter values depending on the algorithm that you choose for your use case.

• For popular algorithms and use cases, you can generally find great guidance on the values of hyperparameters to use from the data science and the research community.

• Once you have validated your choices of algorithm, code, and dataset to solve your machine learning use cases, you can leverage automatic model tuning to fine tune your hyperparameters to find the best performing values.

• few popular algorithms for automatic model tuning:

  Gridsearch

  • To tune your model, you start by defining available hyperparameter sets that include both the name of the hyperparameter and the range of values you want to explore for the hyperparameter.
  • The grid search algorithm tests every combination by training the model on each of the hyperparameters and selecting the best possible parameters.
  • Advantage of the grid search is that it allows you to explore all possible combinations. This idea works really well when you have a small number of hyperparameters and a small range of hyperparameter values to explore for these hyperparameters.
  • However, when the number of hyperparameters increases or the range of values that you want to explore for these hyperparameters increases, this could become very time consuming. The grid search does not scale well to large number of parameters.

  

  Randomsearch

  • To address this issue, you can use random search. In random search, once again, you start by defining the available hyperparameter sets that consists of the name of the hyperparameters and the values that you want to explore. Here, the algorithm, instead of searching for every single combination, picks random hyperparameter values to explore in the defined search space.
  • Additionally, you can also define stop criteria, such as the time elapsed or the maximum number of trainings to be completed. Once the stop criteria is met, you select the best performing set of hyperparameters from the trained models available so far. An advantage of random search is that it is much more faster when compared to the grid search.
  • However, due to the randomness involved in the search process, this algorithm might miss the better performing hyperparameters. When you apply the concept of hyperparameter tuning to classification and regression models, it is very similar to finding the best possible model parameters by minimizing the loss function.

  

  Bayesian Optimization

  • In Bayesian optimization, hyperparameter tuning is treated as a regression problem. The hyperparameter values are learned by trying to minimize the loss function of a surrogate model. Here, the algorithm starts with random values for the hyperparameters and continuously narrows down the search space by using the results from the previous searches.
  • The strength of Bayesian optimization is that the algorithm is much more efficient in finding the best possible hyperparameters because it continues to improve on the results from previous searches.
  • However, this also means that the algorithm requires a sequential execution. There is also a possibility that Bayesian optimization could get stuck in a local minima, which is a very prominent problem when you use techniques like gradient descent for minimizing a loss function.

  

  Hyperband

  • Hyperband is based on bandit approach. Bandit approaches typically use a combination of exploitation and exploration to find the best possible hyperparameters. The strength of the bandit approaches is that dynamic pull between exploitation and exploration.
  • When applied to the hyperparameter tuning problem space, You start with the larger space of random hyperparameter set and then you explore a random subset of these hyperparameters for a few iterations.
  • After the first few iterations, you discard the worst performing half of the hyperparameter sets. In the subsequent few iterations, you continue to explore the best performing hyperparameters from the previous iteration.
  • You continue this process until the set time is elapsed or you remain with just one possible candidate. Hyperband clearly stands out by spending the time much more efficiently than other approaches we discussed to explore the hyperparameter values using the combination of exploitation and exploration.
  • On the downside, it might discard good candidates very early on and these could be the candidate that converge slowly.

  

Tunning a BERT based classifier:

• Sagemaker's automatic model tuning, also called as hyperparameter tuning, finds the best version of the model by running multiple training jobs on your dataset using the hyperparameter range values that you specify.
• Additionally to the hyperparameter tuning job, you can also provide objective metric and tuning strategy. For example, you can specify the objective metric as maximizing the validation accuracy. For tuning strategies, SageMaker natively supports random and Bayesian optimization strategies. You can extend this functionality by providing an implementation of another tuning strategy as a docker container.
• I want to use the random tuning strategy for the hyperparameter tuning.SageMaker behind the scenes, runs multiple training jobs and returns the training job with the best possible validation accuracy.
• There are three steps involved in this process. Creating the PyTorch Estimator, and creating a hyperparameter tuner job, and then finally, analyzing the results from the tuner job.
• Creating a PyTorch Estimator: The Pytorch Estimator will hold a fixed set of hyperparameters that you do not want to tune during this process. These hyperparameters are defined like a dictionary. Once You have a fixed hyperparameter, create a PyTorch estimator and pass in the fixed hyperparameters dictionary to the PyTorch estimator.

Code:

hyperparameters = {
    'epochs':3,
    'train_step_per_epoch':50,
    'validation_batch_size':64,
    'validation_steps_per_epoch':50,
    'freeze_bert_layer':False,
    'seed':42,
    'max_seq_length':64,
    'backend':'gloo',
    'run_validation':True,
    'run_sample_validation':False
}

from sagemaker.pytorch import PyTorch as PyTorchEstimator

estimator = PyTorchEstimator(
    entry_point = 'train.py',
    ...,
    hyperparameters = hyperparameters,
    )

• Creating a hyperparameter tuner job: In this step, I want to define the hyperparameters that I want to tune.

Code:

# Hyperparameter type: Could be categorical or continuous or integer type.
# Categorical Hyperparameter type: If your hyperparameter can take specific categorical values. For example, if you want to test your neural network with different types of optimizers, you can use the categorical variable. You can also use the categorical type with numerical values, if you want to test for specific values instead of a range. For example, for the batch size parameter, if you want to test specific values of 128 and 256 instead of a range. You can treat the batch size as a categorical parameter. If you have any parameters that can take Boolean values like true and false, you can treat that as a categorical type as well.
'train_batch_size': CategoricalParameter([128,256])
'freeze_bert_layer': CategoricalParameter([True,False])

# Integer Hyperparameter type: Use this if you would rather explore a range of values for your parameters. For example, here, for the batch size, I want to explore all the values between 16 and 1024 as batch sizes. I use the integer type here.If the range of values is large to explore, definitely use logarithmic scale to optimize the tuning process.
'train_batch_size': IntegerParameter(16,1024, scaling_type = 'Logarithmic')

# Continuous Hyperparameter type: Example of a continuous type hyperparameter could be learning rate. Here, you're asking the tuning job to explore the values between the low and the high values for the range in a linear scale.
'learning_rate': ContinuousParameter(0.00001, 0.00005, scaling_type = 'linear')

# Define the tunable hyperparameters with the name, type, and the values to explore

from sagemaker.tuner import CategoricalParameter
from sagemaker.tuner import CatinuousParameter
from sagemaker.tuner import IntegerParameter

hyperparameter_ranges = {
    'learning_rate': CatinuousParameter(0.00001, 0.00005),
    scaling_type = 'Linear',
    'train_batch_size': CategoricalParameter([128,256])
}

# Pass those in to the hyperparameter Tuner object:
from sagemaker.tuner import HyperparameterTuner
tuner = HyperparameterTuner(
    estimator = ...,
    hyperparameter_ranges = ...,
    objective_type = ...,
    objective_metric_name = ...,
    strategy = ...,
    )
tuner.fit(inputs={...},...)

• Analyzing the results from the tuner job: Use the tuner object and get the DataFrame to analyze the result

Code:

df_results = tuner.analytics().dataframe()

• Warm start hyperparameter tunning job_ It reuses prior results from a previously completed hyperparameter tuning job or a set of completed hyperparameter tuning jobs to speed up the optimization process and reduce the overall cost. For example, You perform warm start using a single parent, which is a previously completed tuning job. A warm start is particularly useful if you want to change the hyperparameter tuning ranges from the previous job, or if you want to add new hyperparameters to explore. Both these situations can use the knowledge from the previously completed job to speed up the process and find the best model quickly.
• With warm start, there are two different types supported. The first type is identical data and algorithm. When you implement this type, the new hyperparameter tuning job uses the same input data and the training data and the training algorithm as the parent tuning job. You have a chance to update the hyperparameter tuning ranges and the maximum number of training jobs.
• The second type is transfer learning. With this type, the new hyperparameter tuning job uses an updated training data and also can use a different version of the training algorithm. Perhaps you have collected more training data since your last tuning job, and you want to explore the best possible model for the entire training data. Or you may have come across a new algorithm that you would like to explore.

Code:

from sagemaker.tuner import WarmStartConfig
from sagemaker.tuner import WarmStartTypes

warm_start_config = WarmStartConfig(
    warm_start_type = WarmStartTypes.IDENTICAL_DATA_ANDALGORITHM,
    parents = <PARENT_TUNING_JOB_NAME>)

tuner = HyperparameterTuner(
    ...
    warm_start_config = warm_start_config)
    
tuner.fit(...)

Best practices to follow when you train and tune your models on SageMaker:

• Select a small number of hyperparameters; Hyperparameter Tuning, is a time and computation intensive task. The computational complexity is directly proportional to the number of hyperparameters that you tune. SageMaker does allow you to tune up to 20 different hyperparameters at a time. However, choosing a smaller number of hyperparameters to tune will typically yield better results.
• Choose a smaller range of values to explore for the hyperparameters. The values that you choose for the hyperparameters can significantly affect the success of hyperparameter optimization. you will get better results by limiting your search to a small range of values, instead of specifying a large range of values.
• Enable warm start, as discussed when you enable warm start, the hyperparameter tuning job uses results from previously completed jobs to speed up the optimization process and save you the tuning cost.
• Enable early stop. When you enable early stop on the hyperparameter tuning job, the individual training jobs that are launched by the tuning job are dominated early in case the objective metric is not continuously improving. This early stopping of the individual training jobs leads to earlier completion of the hyperparameter tuning job and reduce costs.
• Use small number of concurrent training jobs. SageMaker does allow you to run multiple jobs concurrently during the hyperparameter tuning process. On one hand, if you use a larger number of concurrent jobs, the tuning process will be completed faster. But in fact, the hyperparameter tuning process is able to find best possible results only by depending on the previously completed training jobs. So choose to use a smaller number of concurrent jobs when you're executing these hyperparameter tuning job.
• When training and tuning at scale, it is important to continuously monitor and use the right compute resources.

Best practices to follow to Monitoring Compute resources:

• While you do have the flexibility of using different instance types and instance sizes, how do you determine the exact specific instance type and size to use for your workloads?
• There is really no standard answer for this. It comes down to understanding your workload well and running empirical testing to determine the best possible compute resources to use for your tuning and training workloads.
• SageMaker training jobs emits CloudWatch metrics for resource utilization of the underlying infrastructure. You can use these metrics to observe your training utilization and improve your successive training runs.
• Additionally, when you enable SageMaker Debugger on your training jobs, Debugger provides the visibility into training jobs and infrastructure that is running these training jobs. Debugger also monitors and reports on system resources such as CPU, GPU, and memory, providing you with the very useful insights on the resource utilization and resource bottlenecks.
• You can use these insights and recommendations from Debugger as a guidance to further optimize your training infrastructure.

Checkpointing with Machine learning training:

• Machine learning training is typically a long-time intensive process. It's not uncommon to see training jobs running over multiple hours or even multiple days.
• If these long-running training jobs stop for any reason such as a power failure, or oils fault, or any other unforeseen error, then you'll have to start the training job from the very beginning. This leads to lost productivity.
• Even if you don't encounter any unforeseen errors, there might be situations where you want to start a training job from a known state, to try out new experiments. In these situations, you will use machine learning checkpointing.
• Checkpointing is a way to save the current state of a running training job so the training job, if it is stopped, can be resumed from a known state. Checkpoints are basically snapshots of model in training and include details like model architecture, which allows you to recreate the model training once it stopped, also includes model weights that have been learned in the training process so far. Also, training configuration such as number of epochs that have been executed, and the optimizer used, and the loss observed so far in training, and other metadata information.
• The checkpoints also include information such as optimizer state. This optimizer state allows you to easily resume the training job from where it has stopped.
• When configuring your new training job with checkpointing take two things into consideration, one is the frequency of checkpointing, and the second is the number of checkpoint files you are saving each time.
• If you have a high frequency of checkpointing and saving several different files each time, then you are quickly using up the storage. However, this high frequency and high number of checkpoints you're processing, this state will allow you to resume your training jobs without losing any training state information.
• On the other hand, if the frequency and the number of checkpoints you're saving each time is low, you are definitely saving on the storage space, but there is a possibility that some of the training state has been lost when the training job is stopped.
• When configuring your training jobs with these parameters, take the balance of your storage costs versus your productivity requirements into consideration.

Amazon SageMaker Managed Spot

• Allows you to save training costs. Managed Spot is based on the concept of Spot Instances that offer speed and unused capacity to users at discount prices. SageMaker Managed Spot uses these Spot Instances for hyperparameter tuning and training and leverages machine learning checkpointing to resume training jobs easily.
• Here's how it works. You start a training job on a Docker container on a Spot Instance. Here, you use a training script called train.python. Since Spot Instances can be preempted and terminated with just a two-minute notice, it is important that your train.py file implement the ability to save checkpoints, and the ability to resume from checkpoints.
• SageMaker Managed Spot does the remaining. It automatically backs up the checkpoints to an S3 bucket. In case a Spot Instance is terminated because of lack of capacity, SageMaker Managed Spot continues to pull for additional capacity.
• Once the additional capacity becomes available, a new Spot Instance is created to resume your training and the service automatically transfers all the dataset as well as the checkpoints that are saved into the S3 bucket into your new Instance so that training can be resumed.
• A key thing for you to take advantage of Managed Spot capability is implementing your training script so that they can periodically save the checkpoints and have the ability to resume from a saved checkpoint.

Distributed training strategies:

• Training at scale challenges comes in two flavors, One is the increased training data volume and second is the increased model complexity and model size as a result of the increased training data volume. Using huge amounts of training data and the resulting model complexity could give you a more accurate model. However, there is always a physical limit on the amount of the training data or the size of the model that you can fit on a single computer instance memory.
• Even if you try to use a very powerful CPU or even a GPU instance, increased training data volumes typically means increased number of computations during training process and that could potentially lead to long running training jobs.

• Distributed training is often a technique used to address the scale challenges in distributed training. The training load is split across multiple CPUs and GPUs, also called as devices within a single Compute Node or the node can be distributed across multiple compute nodes or compute instances that form a compute cluster.
• Regardless of whether you choose to distribute the training load within a single compute node or across multiple compute nodes there are two distributed training strategies at play: data parallelism and model parallelism.
• With data parallelism the training data is split up across the multiple nodes that are involved in the training cluster. The underlying algorithm or the neural network is replicated on each individual nodes of the cluster. Now, batches of data are retrained on all nodes using the algorithm and the final model as a result of a combination of results from each individual node.
• In model parallelism, the underlying algorithm or the neural network in this case, is split across the multiple nodes. Batches of data are send to all of the nodes again so that each batch of the data can be processed by the entire neural network. The results are once again combined for a final model.

Code:

# Data parallelism
from sagemaker.pytorch import PyTorch
estimator = PyTorch(
    entry_point = 'train.py',
    role = sagemaker.get_execution_role(),
    framework_version = '1.6.0',
    py_version = 'py3',
    instance_count = 3,
    instance_type = 'ml.p3.16xlarge',
    distribution = {'smdistributed': {'dataparallel': {enabled:True}}}
    )
    
estimator.fit()

# Model parallelism
from sagemaker.pytorch import PyTorch
estimator = PyTorch(
    entry_point = 'train.py',
    role = sagemaker.get_execution_role(),
    framework_version = '1.6.0',
    py_version = 'py3',
    instance_count = 3,
    instance_type = 'ml.p3.16xlarge',
    distribution = {'smdistributed': {'modelparallel': {enabled:True}}}
    )
    
estimator.fit()

Making a selection of which one to use for your specific requirements?

• When choosing a distributed training strategy always keep in mind that if your training across multiple nodes or multiple instances, there is always a certain training overhead. The training overhead comes in the form of internode communication because of the data that needs to be exchanged between the multiple nodes of the cluster.
• If the train model can fit on a single node's memory, then use data parallelism. In the situations where the model cannot fit on a single node's memory, you have some experimentation to do to see if you can reduce the model size to fit on that single node. All of these experimentations will include an effort to resize the model. Some of the things that you can try to resize your model include tuning the hyperparameters.
• Tuning the hyperparameters, such as the number of neural network layers in your neural network, as well as tuning the optimizer to use will have a considerable effect on the final model size. Another thing you can try is reduce the batch size. Try to incrementally decrease the batch size to see if the final end model can fit in a single node's memory.
• Additionally, you can also try to reduce the model input size. If for example, your model is taking a text input, then consider embedding the text with a low dimensional embedded in vector. Or if your model is taking image as an input, try to reduce the image resolution to reduce the model input.
• After trying these various experimentation, go back and check if the final model fits on a single node's memory. And if it does use data parallelism on a single node. Now, even after these experiments if the model is too big to fit on a single node memory, then choose to implement model parallelism.

Options with Amazon Sagemaker:

• Built-in Algorithms: you use the estimator object and to the estimator object, you're passing in the image URI. The image URI is pointing to a container that consist the implementation of the built-in algorithm as well as the training and inference logic.

Code:

estimator = sagemaker.estimator.Estimator(image_uri = image_uri, ...)
estimator.set_hyperparameters(...)
estimator.fit(...)

• Bring your own script: Here, you're using a SageMaker provided container such as a PyTorch container, but you are providing your own training script to be used during training with that particular provided container. Here, you're using the PyTorch container for the estimator and passing in your own script, training.python for the training purposes.

Code:

from sagemaker.pytorch import PyTorch
pytorch_estimator = PyTorch(
    entry_point = 'train.py',
    ...
    )

• Bring your own container: When it is time for you to bring in your own algorithms, you will also create a container and bring that container to be used with a SageMaker. Thats the third option. Bringing your own container to be used with SageMaker consists of four different steps. First, you clear the code that captures all the logic and then you containerize the code. Once you have the container ready, you register the container with Amazon ECR, which is the Elastic Container Registry. Once the container is registered with ECR, you can use the image URI of the registered container with the estimated object. Let's dive a little bit deeper into each one of these steps.
• The first step is codifying your logic. The code should include the logic for the algorithm that you want to implement, as well as the training logic and the inference logic. Once you have the code ready, next step is to containerize your code. Here create a Docker container using the docker build command. Once you have the Docker container ready, next step is to register it with Amazon ECR, which is the Elastic Container Registry.

Code to create a docker container using docker build command:

algorithm_name = tf-custom-container-test
docker build -t${algorithm_name}

• Here, first, you will create a repository to hold all of your algorithm logic as a container and into that repository, you push the container from the previous step using the docker push command. Once the push command is successful, you have successfully registered your container with Amazon ECR. This registered containers can be accessed within image URI that you can use to finally create an estimator.

Code:

aws ecr create-repository --repositry-name"${algorithm_name}">/dev/null
fullname="${account}.dkr.ecr.${region}.amazonaws.com/${algorithm_name}:latest"
docker push${fullname}

# Format for image uri:
byoc_image_uri = '{}.dkr.ecr.{}.{}/{}'.format(account_id,region, uri_suffix, ecr_repository + tag)
estimator = Estimator(image_name = byoc_image_uri, ...)

• Once you have that image URL, you simply create an estimator object by passing in that URI. After this point, using estimator is very similar to how you would use an estimator object with a built-in algorithm, for example.
• Using the four steps that are outlined in this video, you can bring your custom algorithm implementation and train and host the model on the infrastructure that is managed by SageMaker.

Week2

Model deployment options & Strategies:

• Being able to choose the right deployment option that best meets your use case is critical when looking at practical data science for cloud deployments. There are two general options including real-time Inference and batch inference.

Deploying a model for real-time inference in the cloud:

• Deploying a model for real-time inference means deploying it to a persistent hosted environment that's able to serve requests for prediction and provide prediction responses back in real-time or near real-time. This involves exposing an endpoint that has a serving stack that can accept and respond to requests.
• A serving stack needs to include a proxy that can accept incoming requests and direct them to an application that then uses your Inference code to interact with your model. This is a good option when you need to have low latency combined with the ability to serve new prediction requests that come in, so some example use cases here would be fraud detection. Where you may need to be able to identify whether an incoming transactions is potentially fraudulent in near real time or product recommendations. Where you want to be able to predict the appropriate products based on a customer's current search history or a customer's current shopping cart.

How a real time persistent endpoint would apply to your product review use case?

• In our usecase, you need to identify whether a product review is negative and immediately notify a customer support engineer about negative reviews, So that they can proactively reach out to the customer right away. Here you have some type of web application that a consumer enters their product review into. Then that web application or secondary process called by that web application coordinates a call to your real time end point that serves your model with the new product review text. The hosted model then returns a prediction. So in this case it would be a negative class for sentiment that can then be used to initiate a back end process that opens a high severity support ticket to the customer support engineer. Given that your objective here is to have quick customer support response.
• You can see where you would need to have that model consistently available through a real time endpoint that's able to serve your prediction requests that come in. And serve your response traffic.

Deploying a model for batch inference in the cloud:

• With batch inference, You aren't hosting a model that persists and can serve requests for prediction as they come in. Instead, your batch in those requests for prediction, running a batch job against those batch requests and then out putting your prediction responses typically is batch records as well. Then once you have your prediction responses, they can then be used in a number of different ways. Those prediction responses are often used for reporting or are persisted into a secondary data store for use by other applications or for additional reporting.
• Use cases that are focused on forecasting are a natural fit for batch inference. So say you're doing sales forecasting where you typically use batch sales data over a period of time to come up with new sales forecast. In this case, you'd use batch jobs to process those prediction requests and potentially store those predictions for additional visibility or analysis.
• let's go back to your product review case. So let's say your ultimate business goal here is to be able to identify vendors that have potential quality issues by detecting trends for negative product reviews per vendor.
• So in this case, you don't need a real time end point, but you would use a batch inference job to take a batch of product review data. Then run batch jobs at a reasonable frequency that you identify that can take all of those product reviews on input. Process those predictions and that output that data just as the prediction request data is a set of batch records on input.
• The prediction responses that are output to the model are also collected as a collection of batch records. That data could then be persisted so that your analysts could aggregate the data. Run reports to identify any potential issues with vendors that have a large number of negative reviews with your batch job.
• These jobs aren't persisted so they run for only the amount of time that it takes to process those batch requests on input.

Deployment to the edge:

• Is not a cloud-specific. But is a key consideration when deploying models closer to your users or in areas with poor network connectivity. In the case of edge deployments, you train your models in another environment in this case in the cloud and then optimize your model for deployment to edge devices. This process is typically aimed at compiling or packaging your model in a way that is optimized to run at the edge. Which usually means things like reducing the model package size for running on smaller devices. In this case you could use something like Sagemaker Neo to compile your model in a way that is optimized for running at the edge.
• Edge use cases bring your model closer to where it will be used for prediction, so typical use cases here would be like manufacturing, where you have cameras on an assembly line And you need to make real time inferences or in use cases where you need to detect equipment anomalies at the edge. Inference data in this case is often sent back to the cloud for additional analysis or for collection of ground truth data that can then be used to further optimize your model.

	Real-Time inference	Batch inference	Edge
When to use	Low latency real-time predictions (ex: interactive recommendations)	Batch request & response prediction is acceptable for your use case (ex: forecasting)	Models need to deployed to edge devices (ex: Limited connectivity, internet of things)
Cost	Persistent endpoint - pay for resources while endpoint is running	Transient environments - pay for resources for the duration of the batch job	Varies

• The choice to deploy to the edge is typically an obvious one as there's edge devices and you might be working with use cases where there is limited network connectivity. You might also be working with internet of things or IOT use cases or use cases where the cost in terms of the time spent in data transfer is not an option even when it's single digit millisecond response.
• the choice between real time inference and batch inference typically comes down to the ways that you need to request and consume predictions in combination with cost. A real time endpoint can serve real time predictions, where the prediction requests sent on input is unique and requires an immediate response with low latency.
• The trade off is that a persistent endpoint typically cost more because you pay for the compute. And the storage resources that are required to host that model while that endpoint is up and running a batch job in contrast works well when you can batch your data for prediction And that's your responses back, now, these responses can then be persisted into a secondary database that can serve real time applications when there is no need for new prediction requests. And responses per transaction
• so in this case, you can run batch jobs in a transient environment. Meaning that the compute and storage environments are only active for the duration of your batch job.

Model deployment strategies:

• This is important because you want to be able to deploy new models in a way that minimizes risk and downtime while measuring the performance of a new model or a new model version. As an example, if you have a newer version of a model, you typically don't want to deploy that model or that new version in a way that disrupts service. You may also want to monitor the performance of that new model version for a period of time in a way that allows you to seamlessly roll back if there is an issue with that new version.

Common deployment strategies:

• Blue/Green, Shadow, Canary, A/B testing are static approaches to deploying new or updated models. meaning that you manually identify things like when to swap traffic and how to distribute that traffic. However Multi-armed bandit is the approach that is more dynamic in nature, meaning that instead of manually identifying when and how you distribute traffic, you can take advantage of approaches that incorporate machine learning to automatically decide when and how to distribute traffic between multiple versions of a deployed model.
• Blue/Green deployments:
  • With blue/green deployments, you deploy your new model version to a stack that conserved prediction and response traffic coming into an endpoint. Then when you're ready to have that new model version actually start to process prediction requests coming in, you swap the traffic to that new model version.
  • This makes it easy to roll back because if there are issues with that new model or that new model version doesn't perform well, you can swap traffic back to the previous model version.
  • With blue/green deployment, you have a current model version running in production. In this case, we have version 1. This accepts 100 percent of the prediction request traffic and responds with prediction responses. When you have a new model version to deploy, in this case, model version 2, you build a new server or container to deploy your model version into. This includes not only the new model version but also the code in the software that's needed to accept and respond to prediction requests.
  • the new model version is deployed, but the load balancer has not yet been updated to point to that new server hosting the model, so no traffic is hitting that endpoint yet. After the new model version is deployed successfully, you can then shift 100 percent of your traffic to that new cluster serving model version 2 by updating your load balancer.
  • This strategy helps reduce downtime if there's a need to roll back and swap back to version 1 because you only need to re-point your load balancer back to version 1. The downside to this strategy is that it is 100 percent swap of traffic. So if the new model version, version 2, in this case, is not performing well, then you run the risk of serving bad predictions to 100 percent of your traffic versus a smaller percentage of traffic.

• Shadow challenger:
  • in this case, you're running a new model version in production by letting the new version accept prediction requests to see how that new model would respond, but you're not actually serving the prediction response data from that new model version. This lets you validate the new model version with real traffic without impacting live prediction responses.
  • with the shadow or challenger deployment strategy, the new model version is deployed and both versions have 100 percent of prediction requests traffic being sent to each version. However, you'll notice for version 2, only the prediction requests are sent to the model, and you aren't actually serving prediction responses from model version 2.
  • Responses that would have been sent back for model version 2 are typically captured and then analyzed for whether version 1 or version 2 of the model would have performed better against that full traffic load.
  • This strategy also allows you to minimize the risk of deploying a new model version that may not perform as well as model version 1, and this is because you're still able to analyze how version 2 of your model would perform without actually serving the prediction responses back from that model version.
  • Then once you are comfortable that model version 2 is performing better, you can actually start to serve prediction responses directly from model version 2 instead of model version 1.

• Canary deployments:
  • With a canary deployment, you split traffic between model versions and target a smaller group to expose that new model version 2. Typically, you're exposing the select set of users to the new model for a smaller period of time to be able to validate the performance of that new model version before fully deploying that new version out to production.
  • Canary deployment is a deployment strategy where you're essentially splitting traffic between two model versions, and again, with canary deployments, you typically expose a smaller specific group to that new model version while model version 1 still serves the majority of your traffic.
  • Below, you can see that 95 per cent of prediction requests and responses are served by Model Version 1 and a smaller set of users are directed to Model Version 2. Canary deployments are good for validating a new model version with a specific or smaller set of users before rolling it out to all users.

• A/B testing:
  • Canary and A/B testing are similar in that you're splitting traffic. However, A/B testing is different in that typically you're splitting traffic between larger groups and for longer periods of time to measure performance of different model versions over time. This split can be done by targeting specific user groups or just by setting a percentage of traffic to randomly distribute to different groups.
  • Let's take a closer look at A/B testing. With A/B testing, again, you're also splitting your traffic to compare model versions. However, here you split traffic between those larger groups for the purpose of comparing different model versions in live production environments.
  • Here, you typically do a larger split across users. So 50 percent one model version, 50 percent the other model version. You can also perform A/B testing against more than two model versions as well. While A/B testing seemed similar to canary deployments, A/B testing tests those larger groups, and typically runs for longer periods of time than canary deployments.
  • A/B tests are focused on gathering live data about different model versions. to gather that performance data that is statistically significant enough, which provides that ability to confidently roll out Version 2 to a larger percent of traffic.
  • Because you're running multiple models for longer periods of time, A/B testing allows you to really validate your different model versions over multiple variations of user behavior.
  • As an example, you may have a forecasting use case that has seasonality to it. You need to be able to capture how your model performs over changes to the environment over time.
  • A/B tests are typically fairly static and need to run over a period of time. With this, you do run the potential risk of running with a bad or low-performing model for that same longer period of time.

• Multi-Armed Bandits:
  • Multi-armed bandits use reinforcement learning as a way to dynamically shift traffic to the winning model versions by rewarding the winning model with more traffic but still exploring the nonwinning model versions in the case that those early winners were not the overall best models.
  • In this implementation, you first have an experiment manager, which is basically a model that uses reinforcement learning to determine how to distribute traffic between your model versions. This model chooses the model version to send traffic to based on the current reward metrics and the chosen exploit explore strategy.
  • Exploitation refers to continuing to send traffic to that winning model, whereas exploration allows for routing traffic to other models to see if they can eventually catch up or perform as well as the other model. It will also continue to adjust that prediction traffic to send more traffic to the winning model.
  • in this case, your model versions are trying to predict the star rating. You can see Model Version 1 predicted that this was a five-star rating, while Model Version 2 predicted it was a four-star rating. The actual rating was four stars. So in this case Model Version 2 wins. So your multi-arm bandit will reward that model by sending more traffic to Model Version 2.

Amazon sagemaker Hosting: Real-time inference:

• SageMaker hosting includes SageMaker endpoints, these are persistent endpoints that can be used for real-time inference. SageMaker endpoints can be used to serve your models for predictions in real-time with low latency. Serving your predictions in real-time requires a model serving stack that not only has your trained model, but also a hosting stack to be able to serve those predictions.
• That hosting stack typically include some type of a proxy, a web server that can interact with your loaded serving code and your trained model. Your model can then be consumed by client applications through real time invoke API request.
• The request payload sent when you invoke the endpoint is routed to a load balancer and then routed to your machine learning instance or instances that are hosting your models for prediction.
• SageMaker has several built-in serializers and deserializers that you can use depending on your data formats. As an example for serialization on prediction request, you can use the JSON line serializer, which will then serialize your inference requests data to a JSON lines formatted string. For deserialization on prediction response, the JSON deserializer will then deserialize JSON lines data from an inference endpoint response. Finally, response payload is then routed back to the client application.
• With SageMaker model hosting, you choose the machine-learning instance type, as well as the count combined with the docker container image and optionally the inference code, and then SageMaker takes care of creating the endpoint, and deploying that model to the endpoint. The type of machine learning instance you choose really comes down to the amount of compute and memory you need.

• SageMaker has three basic scenarios for deployment when you use it to train and deploy your model. You can use prebuilt code, prebuilt serving containers, or a mixture of the two.

• Deploying a model that was trained using a built-in algorithm
  • In this option, you use both prebuilt inference code combined with a prebuilt serving container. The container includes the web proxy and the serving stack combined with the code that's needed to load and serve your model for real time predictions. This scenario would be valid for some of the SageMaker built-in algorithms where you need only your trained model and the configuration for how you want to host that machine learning instance behind that endpoint.
  • For this scenario to deploy your endpoint, you identify the prebuilt container image to use and then the location of your trained model artifact in S3. Because SageMaker provides these built-in container images, you don't have any container images to actually build for this scenario.

• Deploying a model using a built-in framework like TensorFlow or PyTorch where you're still using prebuilt container images for inference, but with the option of bringing your own serving code as well.
• The next option still uses a prebuilt container that's purpose-built for a framework such as TensorFlow or PyTorch, and then you can optionally bring your own serving code. In this option, you'll notice that while you're still using a prebuilt container image, you may still need or want to bring your own inference code.

• Bringing your own container image and inference code for hosting a model on a SageMaker endpoint
  • you'll have some additional work to do by creating a container that's compatible with SageMaker for inference. Here you'll have some additional work to do by creating a container that's compatible with SageMaker for inference. But this also offers the flexibility to choose and customize the underlying container that's hosting your model.
• All of these options deploy your model to a number of machine learning instances that you specify when you're configuring your endpoint. You typically want to use smaller instances and more than one machine learning instance. In this case, SageMaker will automatically distribute those instances across AWS availability zones for high availability.

Once your endpoints are deployed, how do you then ensure that you're able to scale up and down to meet the demands of your workloads without overprovisioning your ML instances?

• This is where autoscaling comes in. It allows you to scale the number of machine learning instances that are hosting your endpoints up or down based on your workload demands.
• This is also important for cost optimization for two reasons. First, not only can you scale your instances up to meet the higher workload demands when you need it, but you can also scale it back down to a lower level of compute when it is no longer needed. Second, using autoscaling allows you to maintain a minimum footprint during normal traffic workloads, versus overprovisioning and paying for compute that you don't need.
• The on-demand access to compute and storage resources that the Cloud provides allows for this ability to quickly scale up and down.

How autoscaling works?

• When you deploy your endpoint, the machine learning instances that back that endpoint will emit a number of metrics to Amazon CloudWatch. CloudWatch is the managed AWS service for monitoring your AWS resources. SageMaker emits a number of metrics about that deployed endpoints such as utilization metrics and invocation metrics.
• Invocation metrics indicate the number of times an invoke endpoint request has been run against your endpoint, and it's the default scaling metric for SageMaker autoscaling. You can actually define a custom scaling metric as well, such as CPU utilization.
• et's assume you've set up your autoscaling on your endpoint and you're using the default scaling metric of number of invocations. Each instance will emit that metric to CloudWatch. As part of the scaling policy that you can figure.
• If the number of invocations exceeds the threshold that you've identified, then SageMaker will apply the scaling policy and scale up by the number of instances that you've configured. After scaling policy for your endpoint, the new instances will come online and your load balancer will be able to distribute traffic load to those new instances automatically.
• You can also add a cool down policy for scaling out your model, which is the value in seconds that you specify to wait for a previous scaled-out activity to take effect. The scale out cooldown period is intended to allow instances to scale out continuously, but not excessively.
• you can specify a cool down period for scaling in your model as well. This is the amount of time in seconds, again, after a scale-in activity completes, before another scale-in activity can start. This allows instances to scale in slowly.

How you actually set up Autoscalling?

• First, you register your scalable target. A scalable target is an AWS resource, and in this case, you want to scale the SageMaker resource as indicated in the service namespace.
• After you register your scalable target, you need to then define the scaling policy. The scaling policy provides additional information about the scaling behavior for your instances. In this case, you have your predefined metric, which is the number of invocations on your instance, and then your target value, which indicates the number of invocations per machine learning instance that you want to allow before invoking your scaling policy.
• You'll also see the scale-out and scale-in cooldown metrics that I mentioned previously. In this case, you see a scale-out cooldown of 60, which means that after autoscaling successfully scales out, it starts to calculate that cool-down time. The Scaling policy will increase again to that desired capacity until the cool down period ends. The ScaleInCool down setting of 300 seconds means a SageMaker will not attempt to start another cool down policy within 300 seconds when the last one completed.
• In your final step to set up autoscaling, you will apply autoscaling policy, which means you apply that policy to your endpoint. Your endpoint will now be skilled in and scaled out according to that scaling policy that you've defined. You'll notice here you refer to the previous configuration that was discussed, and you'll also see a new parameter called policy type. Target tracking scaling refers to the specific autoscaling type that is supported by SageMaker. This uses a scaling metric and a target value as an indicator to scale. You'll have the opportunity to get hands on your lab for this week in setting up and applying autoscaling to SageMaker endpoints.

Code

autoscale.register_scalable_target(
    ServiceNamespace = "sagemaker",
    ResourceID = "endpoint/" + endpoint_name,
    ScalableDimension = "sagemaker:variant:DesiredInstanceCount",
    MinCapacity = 1,
    MaxCapacity = 2,
    RoleARN = role,
    SuspendedState = {
        "DynamicScalingInSuspended" : False,
        "DynamicScalingOutSuspended" : False,
        "ScheduledScalingSuspended" : False,
    })

# Define scaling policy
    scaling_policy = {
        "TargetValue" : 2.0,
        "PredefinedMatricsSpecification" : {
            "PredefinedmatricType" : # Scaling metric "SageMakerVariantInvocationsPerInstance",
        },
        "ScaleOutCooldown" : 60, # Wait time before beginning another scale out activity after last one completes
        "ScaleInCooldown" : 300, # Wait time before beginning another scale in activity after last one completes
        }
        
# Apply scaling policy
autoscale.put_scaling_policy(PolicyName = ...,
ServiceNamespace = "sagemaker",
ResourceID = "endpoint/" + endpoint_name,
ScalableDimension = "sagemaker:variant:DesiredInstanceCount",
PolicyType = "TargetTrackingScaling",
TargetTrackingScalingPolicyConfiguration = scaling_policy)

multi-model endpoints and inference pipelines

• SageMaker endpoints that serve predictions for one model can also host multiple models behind a single endpoint. Instead of downloading your model from S3 to the machine learning instance immediately when you create the endpoint, with multi-model endpoints SageMaker dynamically loads your models when you invoke them. You invoke them through your client applications by explicitly identifying the model that you're invoking.
• In this case you see the predict function is identifying Model 1 for this prediction request. SageMaker will keep that model loaded until resources are exhausted on that instance. the deployment options around the container image that is used for inference when you deploy a SageMaker endpoint. All of the models that are hosted on a multi-modal endpoint must share the same serving container image.
• Multi-model endpoints are an option that can improve endpoint utilization when your models are of similar size and share the same container image and have similar invocation latency requirements. Inference pipeline allows you to host multiple models behind a single endpoint. But in this case, the models are sequential chain of models with the steps that are required for inference.
• This allows you to take your data transformation model, your predictor model, and your post-processing transformer, and host them so they can be sequentially run behind a single endpoint. As you can see in this picture, the inference request comes into the endpoint, then the first model is invoked, and that model is your data transformation. The output of that model is then passed to the next step, which is actually your XGBoost model here, or your predictor model. That output is then passed to the next step, where ultimately in that final step in the pipeline, it provides the final response or the post-process response to that inference request.
• This allows you to couple your pre and post-processing code behind the same endpoint and helps ensure that your training and your inference code stay synchronized.

Production variants and how they can be used to implement advanced deployment options for your real-time endpoints hosted using Amazon SageMaker hosting:

• Production variants can be used for A/B testing or canary testing. But for this week's labs, you will use them specifically for A/B testing. If you remember, A/B testing is essentially splitting traffic across larger groups for a period of time to measure and compare the performance of different model versions.
• I've shown have mostly shown a single model behind an endpoint. That single model is one production variant. A production variant is a package SageMaker model combined with the configuration that defines how that model will be hosted.
• SageMaker model includes information such as the S3 location of that trained model artifact, the container image that can be used for inference with that model, and the service run-time role and the model's name. The hosting resources configuration includes information about how you want that model to be hosted. This includes things like the number and the type of machine learning instances, a pointer to the SageMaker packaged model, as well as the variant name and variant weight.
• A single SageMaker endpoint can actually include multiple production variants. Production variants can be used for both canary testing and A/B testing, as well as some of the other deployment strategies that I discussed earlier.
• canary deployment includes canary groups, which are smaller subsets of users that are directed to a specific model version to gauge the performance of that model version on a specific group of users.
• In this below picture, you see a SageMaker endpoint that's configured with two variants, variant A and variant B. Variant A has been configured so that 95 percent of the traffic will continue to be served by that model version, but only five percent of traffic will be served by variant B.
• The client application here controls this traffic on which users will be exposed to which variant. This is done programmatically by specifying the target variant when the client application invokes that target endpoint. This is one way to use production variants for canary roll-outs.
• Let's now look at another way to use production variants for A/B testing. This is what you'll also be doing in your lab for this week. In this case, you still have two variants, variant A and variant B. However, in this case, you're splitting traffic equally. 50 percent of traffic is being served by variant A, and 50 percent is being served by variant B. So in this case, the client application just invokes the SageMaker endpoint, and traffic is automatically routed based on that variant weight.
• However, you could also configure it to use the client application to route your 50 percent traffic to specific users as well. A/B testing is similar to what you would do for a canary roll-out, but you'd be doing it for a larger user group and typically running both model versions for a longer period of time while you compare the results of those different model versions.

how you can set up production variants for A/B testing between two model versions.

• you'll learn how to use production variants for the SageMaker option where you're using a pre-built container image. In this first step, you construct the URI for the pre-built Docker container image. For this, you're using a SageMaker provided function to generate the URI of the Amazon Elastic Container Registry image that'll be used for hosting.

Code

import sagemaker

inferance_image_uri = sagemaker.image_uris.retrieve(
    framework = ..., PyTorch, Tensorflow, etc ..., 
    version = '1.6.0',
    instance_type = 'ml.m5.xlarge',
    py_version = 'py3',
    image_scope = 'inference'
    )

• The next step includes creating two model objects which packages each of your trained models for deployment to a SageMaker endpoint. To create the model packages, you'll use the URI information from the previous step and supply a few other items for packaging, such as the location of your trained model artifact is stored in Amazon S3, the AWS identity and access management or the IAM role that will be used by the inference code to access AWS resources.

Code

sm.create_model(
    name= model_name_a,
...)

sm.create_model(
    name= model_name_b,
...)

• Next, you configure the production variants that will be used when you create your endpoint. Each variant points to one of the previously configured model packages, and it also includes the hosting resources configuration.
• you're indicating that you want 50 percent of your traffic sent to model variant A and 50 percent of your traffic sent to model variant B. Recall that the model package, combined with the hosting resources configuration, make up a single production variant. Now that you've configured your production variants, you now need to configure the endpoint to use these two variants.

Code

from sagemaker.session \ 
import production_variant

variantA = production_variant(
    model_name = ...,
    instance_type = ...,
    inital_instance_count = 1,
    variant_name = 'VariantA',
    initial_weight = 50,
)

from sagemaker.session \ 
import production_variant

variantA = production_variant(
    model_name = ...,
    instance_type = ...,
    inital_instance_count = 1,
    variant_name = 'VariantB',
    initial_weight = 50,
)

• In this step, you create the endpoint configuration by specifying the name and pointing to the two production variants that you just configured. The endpoint configuration tells SageMaker how you want to host those models. Finally, you create the endpoint, which uses your endpoint configuration to create a new endpoint with two models or two production variants

Code

endpoint_config = sm.create_endpoint_config(
    EndpointConfigName = ...,
    ProductionVariants = [variantA, varinatB]
    )
    
endpoint_response = sm.create_endpoint(EndpointName = ..., 
EndpointConfigName = ...)

Learn how to deploy your batch use cases Using SageMaker Batch Transform:

• let's start with how batch Transform jobs work. With batch Transform, you package your model first. This step is the same, whether you're going to deploy your model to a SageMaker endpoint, or whether you're deploying it for batch use cases.
• Similar to hosting for SageMaker endpoints, you either use a built-in container for your inference image or you can also bring your own. Your model package contains information about the S3 location of your trained model artifact, and the container image to use for inference.
• Next, you create your transformer. For this, you provide the configuration information about how you want your batch job to run. This also includes parameters such as the size and the type of machine learning instances that you want to run your batch job with, as well as the name of the model package that you previously created. Additionally, you specify the output location, which is the S3 bucket, where you want to store your prediction responses.
• After you've configured your transformer, you're ready to start your batch transformed job. This can be done on an ad hoc basis or scheduled as part of a normal process. When you start your job, you provide the S3 location of your batch prediction requests data. SageMaker will then automatically spin up the Machine Learning instances using the configuration that you supplied, and it will process you're batch requests for prediction.
• When the job is complete, SageMaker will automatically output the prediction response data to the S3 location that you specified and spin down the Machine Learning Instances.
• Batch jobs operate in a transient environment, which means that the Compute is only needed for the time it takes to complete the batch Transform job. Batch Transform also has more advanced features such as inference pipeline. If you recall, inference pipeline allows you to sequentially chain together multiple models.
• You can combine your steps for inference within a single batch job so that the batch job includes your data transformation model for transforming your input data into the format expected by the model, the actual model for prediction, and then potentially a data post-processing model that transforms the labels that will be used as your inference response and put to your S3 bucket for output.
• SageMaker endpoints, you can use the feature called inference pipeline to combine multiple models to run sequentially during your batch job.

Model integration patterns for integrating your client applications with your deployed machine learning models.

• once you have your model deployed to an endpoint, what are some of the considerations for integrating your models with client applications? When you deploy a model to an endpoint, that model is trained using specific features that have been engineered for model performance, and also to ensure that those features are in the required format and are understandable to your machine learning algorithm. The same transformations that were applied to your data for training, need to be applied to your prediction requests that are sent to that same deployed model.
• with your product review use case, if you were to send the exact text payload with the string of, "I simply love it" into your hosted model, you would get an error. This is because your model was trained on data in a specific format. Without performing a data transformation to put it in that same format that is expected for inference, you'll get an error because your model can't understand that text data.
• To fix this you'd need to apply those same data transformations that were applied when you trained your model, to your product review text before you send it to the model for prediction. There's a number of ways that you can do this, but let's look at one potential method.

• In this case, you're relying on your client applications to transform that prediction requests data into the correct format before it's actually sent to the endpoint for inference.
• All this would work if your client code always remains synchronized with your training code. It's difficult to scale when you have multiple applications or teams that interface with your model.
• As you can imagine, it's challenging in this case to always ensure that your data preprocessing code stays synchronized with the data preprocessing code used for training your model.
• Another consideration here is you may also still need to convert that model prediction response into a format that's readable by your client application. As an example, you're model here will return a one for positive, but your client applications know that a class of one actually translates into positive.
• Let's look at another option. You could implement a back-end function or process that runs before you reach the endpoint, that hosts your model for prediction. This is a common implementation pattern, but you still need to ensure that your data transformation code or the transformer model that runs before the formatted prediction request is sent to your endpoint, always stay synchronized with your trained model.
• Finally, you can also couple your data preprocessing transformers with your model by hosting them behind the same endpoint. In this case, because your data preprocessing is tightly coupled and hosted as well as deployed along with your model. It helps ensure that your training and your inference code stay synchronized, while abstracting the complexity away from the machine learning client applications that integrate with your model.

considerations for Monitoring ML models:

• Machine learning workloads have some similarities with other type of workloads. We will cover some of the unique considerations here by focusing on three core areas of monitoring, including business, system, and model monitoring.

Monitoring models:

• Why do you need to monitor models? Models decay over time for a number of reasons. they typically relate to some type of change in the environment where the model was originally trained.

• the trained models make predictions based on old information or they are able to adapt to changes in the environment over time.
• examples for what causes models to degrade?
• First, you can have a change in customer behavior. So let's say you have a model that's trying to predict which products a specific customer might be interested in. Customer behavior can change drastically and quickly, based on a number of factors such as life changes or the economy, just to name a few. They may be interested in new products based on those life changes. And if you're using a model that's trained on old data, the likelihood of providing timely and relevant recommendations goes down.
• Next, you could have changing business environments, let's say your company acquired a new product line.
• Finally, you could have a sudden change in the upstream data as a result of a changing data pipeline. So let's say you ingest raw data that's used to train your model for multiple sources and suddenly a feature that is used to train your model no longer appears in your ingested data. All of these examples and many more can lead to model decay.

how can we monitor for signals of model decay?

• You often hear about two types of monitors when it comes to monitoring machine learning models. The first is concept drift and the second is data drift.

• COncept drift: At a high level, concept drift happens when the environment you trained your model in no longer reflects the current environment.
• In this case, the actual definition of a label changes depending on a particular feature, such as geographical location or age group.
• When you have a model that predicts information in a dynamic world, the underlying dynamics of that world can shift, impacting the target your machine learning model is trying to predict. A method for detecting concept drift includes continuing to collect ground truth data that reflects your current environment.
• And running this labeled ground truth data against your deployed model to evaluate your model performance against your current environment.
• Here, you're looking to see if the performance metric that you optimize for during training like accuracy still performs within an acceptable range for your current environment.
• DAta drift: With data drift, you're looking for changes in the model input data or changes to the feature data.
• So you're looking for signals that the serving data has shifted from the original expected data distribution that was actually used for training. This is often referred to as training serving skew.

• There are many methods to help with this level of monitoring. One is an open source library called Deequ, which performs a few steps to detect signs of data drift. First, you do data profiling to gather statistics about each feature that was used to train the model. So collecting data like the number of distinct values for categorical data or statistics like min and max for numeric features.
• Using those statistics that are gathered during that data profiling, constraints get established to then identify the boundaries for normal or expected ranges of values for your feature data.
• Finally, by using the profile data in combination with the identified constraints, you can then detect anomalies to determine when your data goes out of range from the constraints.
• We have covered two common types of model monitors for detecting concept and data drift. This isn't inclusive of every model monitor or method for monitoring your models. But it gives you a good idea of monitors you should consider to detect for potential signs of model decay.

system monitoring:

• system monitoring is also key to ensuring your models in the surrounding resources that are supporting your machine learning workloads are monitored for signals of disruption as well as potential performance decline.
• You want to ensure you include system monitoring so that you can make sure that the surrounding and underlying resources that are used to host your model are healthy and functioning as expected.
• This includes monitoring things like model latency, which is the time it takes for a model to respond to a prediction request.
• This also includes system metrics for the infrastructure that's hosting your model, so things like CPU utilization.
• Finally, another example is monitoring your machine learning pipelines so that you know, if there are any potential issues with model retraining or deploying a new model version.
• These are just some of the examples of system monitoring you would need to consider as part of your overall monitoring strategy for your machine learning workloads.

monitoring or measuring of business impact

• With this, you're looking at ensuring your deployed model is actually accomplishing what you intend for it to do, which ties back to the impact to your business objectives. This can be difficult to monitor or measure depending on the use case, but let's say you have excess stock of a particular item.
• And you want to get rid of that excess stock by offering coupons to customers who are likely to be interested in that particular product. The model you're building in this case will predict which users are likely to respond to that offer.
• You can typically identify how much stock you have before sending those target coupons, then see how many of the customers you send coupons to actually bought the product. As well as the impact that it had on your stock of products.

Model monitoring using Amazon sagemaker model monitors:

• Monitoring your deployed machine learning model using amazon sagemaker model monitors.
• Model monitor includes four different monitor types including data quality, to monitor drift in data model quality to monitor drift in model quality metrics.
• Statistical bias drift, which is used to monitor signs of statistical bias drift in your model predictions and finally feature attribution drift. Which is used to monitor drift in features.

• Data quality With the data quality, monitor your monitoring for signals that the feature data that was used to train your models has now drifted or is statistically different from the current data that's coming in for Inference model monitor uses DQ.
• Which is an open source library built on Apache spark that performs data profiling generates constraints based on that data profile. And then detects for anomalies when data goes out of bounds from the expected values or constraints.

How to set up the data quality monitor for your hosted Sage maker employments.

• To start, you need to enable data capture on your end point, which tells your endpoint to begin capturing the prediction request data coming in and the prediction response data.
• You can also identify a sampling percentage which is the percentage of traffic that you want to capture.
• Next you create a baseline which actually runs a Sage maker processing job that runs DQ to capture statistics about your data, that was used to train your model.
• Once that baseline runs the output includes statistics about each feature of your training data. So depending on the features there will be statistics relevant to that feature type.
• As an example for our numeric data, you'll see statistics like the min or the max or as for string or categorical data, you'll see statistics on missing or distinct values. The baseline job also automatically identifies constraints based on the statistics discovered.
• You can optionally add or modify constraints based on your domain knowledge as well. These constraints are then used to evaluate or monitor for potential signs of data drift.
• In the next step, you set up the monitoring schedule which identifies how often you want to analyze your inference data against the established baseline. More specifically against those constraints that have been identified.
• the monitoring job outputs results, which includes statistics in any violations against your identified constraints. That information is also captured in amazon cloud watch as well so that you can set up alerts for potential signs of data drift.

data_capture_config = DataCaptureConfig(
    enable_capture = True,
    sampling_percentage = 100,
    destination_s3_uri = s3_capture_upload_path)

predictor = model.deploy(
    initial_instance_count = 1,
    instance_type = 'ml.m4.xlarge',
    endpoint_name = endpoint_name
    data_capture_config = data_capture_config)

• Model quality With the model quality monitor, you're actually using new ground truth data that is collected to evaluate against your deployed model for signs of concept drift.
• So here you use the new label data to evaluate your model against the performance metric that you've optimized for during training, which could be something like accuracy.
• You then compare the new accuracy value to the one you identify during model training to save your accuracy is potentially going down.
• the general steps in model monitor include the same steps that you saw before with data quality where you enable data capture on your end point, create a model quality baseline and then set up a monitoring schedule.
• you also need to ensure for model quality that you have a method in place to collect and ingest new ground truth data that can be used to evaluate your model performance on that new label data.

• Concept drift/Statistical bias drift The statistical bias drift monitor, monitors for predictions for signals of statistical bias. And it does this by integrating with Sage maker, clarify again, the process to set up this monitor is similar to the others.
• In this case you create a baseline that is specific to bias drift and then schedule your monitoring jobs just like the other monitoring types.
• Data drift/ Feature attribution drift monitors for drift in your features for this model, monitor, monitors drift by comparing how the ranking of individual features changed from the training data to the live data. This helps explain model predictions over time.
• the steps for this model monitor type are similar to the others. However, the baseline job in this case uses SHAP behind the scenes SHAP or shapely additive explanations is a common technique used to explain the output of a machine learning model.

Human in the loop:

• importance of data labeling, common data labeling types, and data labeling challenges. how to perform data labeling at scale using human workforces and apply data labeling best practices.
• discuss the concept of automatic data labeling and active learning for efficient data labeling at scale.
• concept of human in the loop pipelines and how they can help you review machine learning model predictions.
• discuss how humans can be part of your machine learning pipelines and how they can help to further scale and improve the quality of your models.
• Two examples: One is discuss the importance of data labeling and how you can leverage human workforces to help you label your data at scale.
• Second is how you can build human in the loop pipelines to review model predictions.
• Amazon SageMaker Ground Truth for building data labeling workflows, and Amazon Augmented AI or Amazon A2I, for implementing human in the loop pipelines.

Data labeling:

• Prior to building training and deploying machine learning models, you need data. successful models are built on high quality training data. But collecting and labelling the training data sets involves a lot of time and effort.
• To build training data sets you need to evaluate and label a large number of data samples. These labeling tasks are usually distributed across more than only one person, adding significant overhead and cost. If there are incorrect labels, the system will learn from the bad information and make inaccurate predictions.

concept of data labeling, common data labeling types, its challenges and ways to efficiently label data

• data labeling is the process of identifying raw data such as images, text files, and videos, among others, and adding one or more meaningful and informative labels to the data.
• For supervised learning to work, you need a label set of data that the model can learn from, so it can make the correct decisions.
• In machine learning, a properly labeled data set that you use as the objective standard to train and assess a given model is often called the ground truth.
• The accuracy of your trained model will depend on the accuracy of your ground truth. So spending the time and resources to ensure highly accurate data labeling is essential.
• When building a computer vision system, you need to label images or pixels or create a border that fully encloses a digital image known as a bounding box to generate your training data set. You can classify images by type, such as an image showing either a scene from a basketball or a soccer game. Or you can classify images by content defining what's actually in the image itself, such as a human and a vehicle in the example shown here.
• These are examples of single label and multi label classification. You can also segmented image at the pixel level. The process known as semantic segmentation identifies all pixels that fall under a given label and usually involves applying a colored filler or mask over those pixels. You can use labeled image data to build a computer vision model to automatically categorize images, detect the location of objects, or segment an image.
• If you need to label video data, you can choose between video classification and video object detection tasks. In a video classification task, you categorize your video clips into specific classes, such as whether the video shows a scene from a concert or sports. In video object detection tasks, you can choose between bounding box, where workers draw bounding boxes around specified objects in your video. Polygon, where you draw polygons around specified objects in your video, such as shown here with the cars example.
• Polyline, where you draw polylines around specified objects in your video, as shown here in the running track video. Or key point, where you draw key points around specified objects in your video, as shown here in the volleyball game example. Instead of just detecting objects, you can also track objects in video data using the same data labeling techniques shown here.
• The difference is that instead of looking at the video on an individual video frame by frame basis, you track the movement of objects in a sequence of video frames.
• In natural language processing, you identify important sections of text or text the text with specify labels to generate your training data set. For example, you may want to identify the sentiment or intent of a text. In a single label classification task, this might be assigning the label positive or negative to a text. Or you might want to assign multiple labels such as positive and inspiring to the text. This would be an example of multi-label classification.
• With named entity recognition, you apply labels two words within a larger text, for example, to identify places and people.
• Natural language processing models are used to a text classification, sentiment analysis, named entity recognition, and optical character recognition. The biggest challenge in data labeling is the massive scale.
• Machine learning models need large labeled data sets. This could be tens of thousands of images to train a computer vision model of thousands of documents to fine tune a natural language model.
• Another challenge is the need for high accuracy. Machine learning models depend on accurately labeled data.
• If there are incorrect labels. Again, the system will learn from the bad information and make an accurate prediction. A third challenge is time. Data labeling is time consuming. As discussed, building a training data set can take up to 80% of the data scientists time.
• To address the previously mentioned challenges, you can combine human labelers with managed data labeling services. These data labeling services provide additional tools to scale the labelling efforts for access to additional human workforces.
• Train a model based on human feedback, so it can perform automated data labeling. And increase the labeling quality by offering additional features to assist the human labelers.

how you can perform data labeling with Amazon SageMaker Ground Truth.

• Ground Truth provides a managed experience where you can set up an entire data labeling job with only a few steps. it helps you efficiently perform highly accurate data labeling using data stored in Amazon Simple Storage Service or Amazon S3, using a combination of automated data labeling and human performed labeling.
• You can choose to use a crowdsourced Amazon Mechanical Turk workforce of over 500,000 labelers, a private team of your co-workers, or one of the third-party data labeling service providers listed on the AWS marketplace, which are pre-screened by Amazon.
• It is very easy to create a data labeling job and it takes only minutes via the AWS management console or call to an application programming interface or API.
• First, as part of the data labeling job creation, you provide a pointer to the S3 bucket that contains the input dataset to be labeled, then, you define the labeling task and provide relevant labeling instructions.
• Ground Truth offers templates for common labeling tasks, where you need to click only a few choices and provide minimal instructions on how to get your data labeled.
• Alternatively, you can create your own custom template. As the last step of creating a labeling job, you select a human workforce.
• You can choose between a public crowdsourced workforce, a curated set off third-party data labeling service providers, or your own workers through a private workforce team.
• Once the labeling job is completed, you can find the labeled data set in Amazon S3.

individual steps

• first step in creating the data labeling job is to set up the input data that you want to label. You store your input dataset in Amazon S3 buckets. In the automated data setup, you only need to provide the S3 location of the dataset you want to label and Ground Truth will identify the dataset and connect the data to your labeling job.
• As part of this automated setup, Ground Truth creates an input manifest file which identifies the objects you want to label. If you choose manual data setup, you have to provide your own manifest file.
• An input manifest file contains the list of objects to label. Each line in the manifest file is a complete and valid JSON object and contains either a source-ref or a source JSON key. The source-ref value points to the S3 object to label.
• This approach is commonly used for binary objects, such as images in your S3 bucket. The source key is used if the object to label is text. In this case, you can start the text directly as the value in the manifest file.
• Once the labeling job is complete, Ground Truth will create an output manifest file in the S3 bucket as well. The output file will contain the results of the labeling job.
• Next, you need to select the labeling task. This defines the type of data that needs to be labeled. Ground Truth has several built-in task types which also come with a corresponding pre-built worker tasked templates.
• If you need to label image data, you can choose between single-label or a multi-label image classification tasks, bounding boxes, semantic segmentation, or label verification, where workers verify existing labels in your dataset. If you need to label video data, you can choose between video clip classification, video object detection, and video object tracking tasks.
• In video object detection and tracking tasks, you can choose between bounding box, polygon, polyline, or key point. If you need to label text data, you can choose between single label or multi-label text classification or a named entity recognition.
• You can also define a custom labeling task when the pre-defined tasks don't meet your needs. Ground Truth provides templates that you can use as a starting point. You can also create custom HTML if necessary.

• how you can build a custom labeling task. As part of the custom labeling task, you create custom AWS Lambda functions.
• The run before and after each data object is sent to the worker. The pre-annotation Lambda function is initiated for and pre-processes each data object sent to your labeling job, before sending it to the workers.
• The post annotation Lambda function processes the results once worker submit a task. If you specify multiple workers per data object, this function may include a logic to consolidate the annotations.

• The next step in defining the data labeling job, is to select the human workforce. Use the workforce of your choice, to label your dataset.
• You can choose your workforce from the following options. You can use the Amazon Mechanical Turk workforce of over 500,000 independent contractors worldwide. If your data requires domain knowledge, or has sensitive data, you can use a private workforce based on your employees or coworkers.
• You can also choose a vendor company in the AWS marketplace, that specializes in data labeling services. These vendor companies are pre-screened by AWS, and have gone for a SOC 2 compliance, and an AWS security review.
• By default, a workforce isn't restricted to specific IP addresses. You can use the update workforce operation, to require that workers use a specific range of IP addresses to access tasks. Workers who attempt to access tasks using any IP address outside the specified ranges, are denied and they get a not found error message on the worker portal.

how you can set up a private workforce using the AWS management console

• First, navigate to the Amazon SageMaker UI, and select "Labeling workforces" on the Ground Truth menu. Then select "Private" and click "Create private team." The next dialog box will present you with the available options for creating a private team by Amazon Cognito, or OpenID Connect. Amazon Cognito provides authentication, authorization, and user management for apps. This enables your workers to sign indirectly to you the labeling UI with a username and a password. You can use Amazon Cognito to connect to your enterprise identity provider as well. The second option for worker authentication is OpenID Connect, or OIDC, which is an identity layer built on top of the OAuth 2.0 Framework. You can use this option to set up a private work team, with your own identity provider. To finish the workforce setup, you'll provide a team name, and invite new workers by email, or you can import workers from an existing Amazon Cognito user group. You can also enable notifications to notify your workers about available work. The last step in defining the data labeling job, is to create the labeling task UI, which presents the human labeler with a data to label and the labeling instructions.
• You can think of it as the instructions page. The human task UI is defined as an HTML template. If you have chosen one of the built-in labeling tasks, you can use a predefined tasks template and customize it to your needs. You can also write a custom task template. You can create the task template using HTML, CSS, JavaScript, the Liquid template language, and Crowd HTML Elements. Liquid is used to automate the template.
• Crowd HTML Elements can be used to include common annotation tools, and to provide the logic you will submit to Ground Truth. The easiest way to get started is to use one of the available samples or built-in templates and customize it to your needs.

techniques to improve the efficiency and accuracy of data labeling.

• Communicate with the labelers through labeling instructions and make them clear to help ensure high-accuracy.
• Consolidate annotations to improve label quality. Labeler consensus helps to counteract the error of individual annotators. Labeler consensus involves sending each data set object to multiple annotators and then consolidating the annotations into a single label.
• Audit labels to verify the accuracy of labels and adjust them as necessary. Use automated data labeling on large data sets. Active learning is the machine learning technique that identifies data that should be labeled by a workers. In ground truth, this functionality is called automated data labeling.
• Automated data labeling helps to reduce the cost and time that it takes to label your data set compared to using only humans.
• Automated data labeling is most appropriate when you have thousands of data objects. The minimum number of objects allowed for automated data labeling is 1,250, but it's better to start with 5,000 objects.
• Another technique to improve the efficiency of data labeling is to reuse prior labeling jobs to create hierarchical labels. This chaining of labeling jobs allows you to use the prior jobs output manifest file as the new jobs input manifest. For example, a first data labeling job could classify objects in an image into cats and dogs. In a second labeling job, you filter their images that contain a dog and add additional labels for the specific dog breed.

communicate with the labelers through labeling instructions and make them clear to help ensure high accuracy.

• For example, provide samples of good and bad annotations. Also, minimize the choice of labels and show only the relevant labels when you assign them to workers. When you define the labeling job, you can also specify how many workers receive the same object to label, this will help to counteract the error in individual annotations. If you send the same task to more than one worker, you need to implement a method for consolidating the annotations.
• Ground truth provides built-in annotation consolidation logic using consensus-based algorithms. Ground truth provides an annotation consolidation function for each of its predefined labeling tasks, bounding box, image classification named entity recognition, semantic segmentation, and text classification. You can also create custom annotation consolidation functions. In general, these functions first assess the similarity between annotations and then assess the most likely label. In the example shown here, the same text classification task can result in different labels from labeler A, B, and C. In the case of discreet mutually exclusive categories, such as the example shown here, this can be straightforward. One of the most common ways to do this is to take the results of a majority vote between the annotations this weighs the annotations equally.

Audit labels to verify accuracy and adjust the labels as necessary:

• Assembled data labeling pipeline for achieving this could look like this. In step 1, unlabeled data is labeled via your choice of work team to create the initial labels. In step 2, an audit and adjustment workflow for improving quality can be set up to review and adjust labels. Note that by default, ground truths processes all objects in your input manifest file again in this setup. You can also filter out objects you don't want to verify to reduce time and costs. New adjusted labels are appended to the initial labels from step 1.
• This makes it easier to calculate the Delta and measure key performance indicators, KPIs. In step 3, you can then use the random selection capability in ground truth to perform an audit that will calculate an error RAID based on the sample data. Automated data labeling is optional, when you choose automated data labeling, part of your data set is labeled using active learning. Again, active learning is a technique that identifies data objects that need to be labeled by humans and data objects that can be labeled by a machine learning model. In this process, a machine learning model for labeling data is first trained on a subset of your raw data that humans have labeled. Where the labeling model has high confidence in its results based on what he has to learn so far, it will automatically apply labels to the raw data. Where the labeling model has lower confidence in its results, it will pass the data to humans to do the labeling. The human generated labels are then provided back to the labeling model for it to learn and improve its ability to automatically label the next set of raw data. Over time, the model can label more and more data automatically and substantially speed up the creation of training datasets. Note that the minimum number of objects allowed for automated data labeling is 1,250, but it is better to have 5,000 objects. The machine learning model for performing the automated data labeling is trained using a SageMaker built-in algorithm that depends on the use case. For example, the model uses the BlazingText algorithm for text classification tasks or the built-in object detection algorithm for bounding box tasks. Another technique for improving the efficiency of data labeling is to reuse prior labeling jobs to create hierarchical labels. This chaining of labeling jobs allows you to use the prior jobs output manifest file as the new jobs input manifest. For example, as shown here, a first data labeling job could classify objects in an image into cats and dogs, and in a second labeling jobs, you could filter the images that contain a dog and that an additional label for the specific dog breed. The result of the second labeling job is an augmented manifest file. Augmented refers to the fact that the manifest file contains the labels from both labeling jobs.