Deep Learning Nanodegree - SageMaker Deployment and more

This repo is a copy of Udacity’s Deep Learning Nanodegree - SageMaker Deployment repo. Its original README.md is here. As part of my exploration of this repo, I make this copy to document my experience and notebook execution in SageMaker. Here is the list of tutorial, mini project and the final project this repo contains. It is the same list on the original README.md but with the links pointing to the current repo.

Tutorials

• Boston Housing (Batch Transform) - High Level is the simplest notebook which introduces you to the SageMaker ecosystem and how everything works together. The data used is already clean and tabular so that no additional processing needs to be done. Uses the Batch Transform method to test the fit model.
• Boston Housing (Batch Transform) - Low Level performs the same analysis as the low level notebook, instead using the low level api. As a result it is a little more verbose, however, it has the advantage of being more flexible. It is a good idea to know each of the methods even if you only use one of them.
• Boston Housing (Deploy) - High Level is a variation on the Batch Transform notebook of the same name. Instead of using Batch Transform to test the model, it deploys and then sends the test data to the deployed endpoint.
• Boston Housing (Deploy) - Low Level is again a variant of the Batch Transform notebook above. This time using the low level api and again deploys the model and sends the test data to it rather than using the batch transform method.
• IMDB Sentiment Analysis - XGBoost - Web App creates a sentiment analysis model using XGBoost and deploys the model to an endpoint. Then describes how to set up AWS Lambda and API Gateway to create a simple web app that interacts with the deployed endpoint.
• Boston Housing (Hyperparameter Tuning) - High Level is an extensions of the Boston Housing XGBoost model where instead of training a single model, the hyperparameter tuning functionality of SageMaker is used to train a number of different models, ultimately using the best performing model.
• Boston Housing (Hyperparameter Tuning) - Low Level is a variation of the high level hyperparameter tuning notebook, this time using the low level api to create each of the objects involved in constructing a hyperparameter tuning job.

Mini-Projects

• IMDB Sentiment Analysis - XGBoost (Batch Transform) is a notebook that is to be completed which leads you through the steps of constructing a model using XGBoost to perform sentiment analysis on the IMDB dataset.
• IMDB Sentiment Analysis - XGBoost (Hyperparameter Tuning) is a notebook that is to be completed and which leads you through the steps of constructing a sentiment analysis model using XGBoost and using SageMaker’s hyperparameter tuning functionality to test a number of different hyperparameters.

Project

Sentiment Analysis Web App is a notebook and collection of Python files to be completed. The result is a deployed RNN performing sentiment analysis on movie reviews complete with publicly accessible API and a simple web page which interacts with the deployed endpoint. This project assumes that you have some familiarity with SageMaker. Completing the XGBoost Sentiment Analysis notebook should suffice.

Setup Instructions

Follow the setup instruction in the original README.md but use this repo instead of the original one:

cd SageMaker
git clone https://github.com/darienmt/DLND-sagemaker-deployment.git
exit

Enjoy!