denotevn / Machine-Learning-For-Production-2022-Coursera

This is courses about Machine Learning for production in Coursera

Machine-Learning-For-Production-2022

WHAT YOU WILL LEARN:

• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
• Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
• Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. Become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Applied Learning Project

• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements
• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application
• Build data pipelines by gathering, cleaning, and validating datasets
• Implement feature engineering, transformation, and selection with TensorFlow Extended
• Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas
• Apply techniques to manage modeling resources and best serve offline/online inference requests
• Use analytics to address model fairness, explainability issues, and mitigate bottlenecks
• Deliver deployment pipelines for model serving that require different infrastructures
• Apply best practices and progressive delivery techniques to maintain a continuously operating production system

Course 1: Introduction to Machine Learning in Production

• In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
• Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skil
• Week 1: Overview of the ML Lifecycle and Deployment
• Week 2: Selecting and Training a Model
• Week 3: Data Definition and Baseline

Course 2: Machine Learning Data Lifecycle in Production

• Week 1: Collecting, Labeling, and Validating data
• Week 2: Feature Engineering, Transformation, and Selection
• Week 3: Data Journey and Data Storage
• Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

Course 3: Machine Learning Modeling Pipelines in Production

• Week 1: Neural Architecture Search
• Week 2: Model Resource Management Techniques
• Week 3: High-Performance Modeling
• Week 4: Model Analysis
• Week 5: Interpretability

Course 4: Deploying Machine Learning Models in Production

In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.

• Week 1: Model Serving Introduction
• Week 2: Model Serving Patterns and Infrastructures
• Week 3: Model Management and Delivery
• Week 4: Model Monitoring and Logging