prasanjit- / ml_notebooks

ml_notebook collection

Machine Learning Deployment using AWS SageMaker

Learning Resources for DevOps, SRE, Cloud & Engineering Management

This repo was created for self-learning ML on AWS Sagemaker

Check for related posts at www.binpipe.org

This repository contains code and associated files for deploying ML models using AWS SageMaker. This repository consists of a number of tutorial notebooks for various coding exercises, mini-projects, and project files.

Table Of Contents

Tutorials I used

• Simple notebook for starting off with Sagemaker kmeans_mnist.ipynb - [https://binpipe.blogspot.com/2019/11/clustering-k-mean-mnist-images-of.html]

• This repository consists of a number of tutorial notebooks for various coding exercises, mini-projects, and project files that will be used to supplement the lessons of the ML Nanodegree at Udacity. [https://github.com/prasanjit-/ml_notebooks/tree/master/sagemaker-notebooks-udacity]

.
├── LICENSE
├── Mini-Projects
│   ├── IMDB\ Sentiment\ Analysis\ -\ XGBoost\ (Batch\ Transform)\ -\ Solution.ipynb
│   ├── IMDB\ Sentiment\ Analysis\ -\ XGBoost\ (Batch\ Transform).ipynb
│   ├── IMDB\ Sentiment\ Analysis\ -\ XGBoost\ (Hyperparameter\ Tuning)\ -\ Solution.ipynb
│   ├── IMDB\ Sentiment\ Analysis\ -\ XGBoost\ (Hyperparameter\ Tuning).ipynb
│   ├── IMDB\ Sentiment\ Analysis\ -\ XGBoost\ (Updating\ a\ Model)\ -\ Solution.ipynb
│   ├── IMDB\ Sentiment\ Analysis\ -\ XGBoost\ (Updating\ a\ Model).ipynb
│   └── new_data.py
├── Project
│   ├── README.md
│   ├── SageMaker\ Project.ipynb
│   ├── Web\ App\ Diagram.svg
│   ├── serve
│   │   ├── model.py
│   │   ├── predict.py
│   │   ├── requirements.txt
│   │   └── utils.py
│   ├── train
│   │   ├── model.py
│   │   ├── requirements.txt
│   │   └── train.py
│   └── website
│       └── index.html
├── README.md
└── Tutorials
    ├── Boston\ Housing\ -\ Updating\ an\ Endpoint.ipynb
    ├── Boston\ Housing\ -\ XGBoost\ (Batch\ Transform)\ -\ High\ Level.ipynb
    ├── Boston\ Housing\ -\ XGBoost\ (Batch\ Transform)\ -\ Low\ Level.ipynb
    ├── Boston\ Housing\ -\ XGBoost\ (Deploy)\ -\ High\ Level.ipynb
    ├── Boston\ Housing\ -\ XGBoost\ (Deploy)\ -\ Low\ Level.ipynb
    ├── Boston\ Housing\ -\ XGBoost\ (Hyperparameter\ Tuning)\ -\ High\ Level.ipynb
    ├── Boston\ Housing\ -\ XGBoost\ (Hyperparameter\ Tuning)\ -\ Low\ Level.ipynb
    ├── IMDB\ Sentiment\ Analysis\ -\ XGBoost\ -\ Web\ App.ipynb
    ├── Web\ App\ Diagram.svg
    └── index.html

• Coding exercises and calculations used during the course AWS SageMaker, Machine Learning and AI with Python by Chandra Lingam from PacktPub [https://github.com/prasanjit-/ml_notebooks/tree/master/fm-pca-xgboost]

BINPIPE aims to simplify learning for those who are looking to make a foothold in the industry. 
Write to me at [e-mail] if you are looking for tailor-made training sessions. 
For self-study resources look around in this repository, the Binpipe Blog and Youtube Channel.

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📒 Maintainer: Prasanjit Singh | www.binpipe.org

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