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.
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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]
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├── 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 nixgurus@gmail.com if you are looking for tailor-made training sessions. For self-study resources look around in this repository, the Binpipe Blog and Youtube Channel.
📒 Maintainer: Prasanjit Singh | www.binpipe.org
