FOLDER - 0 Software Fundamentals
- Some basic items on optimizing code and OOP syntax
- Creating Python packages
FOLDER - 1 Machine Learning in SageMaker
- Deployment of ML models in Amazon SageMaker
- 1.01_Boston Housing - XGBoost - High Level
- Building a model using Batch Transform in SageMaker - use the Python SDK to interact with SageMaker
- 1.02_IMDB Sentiment Analysis - XGBoost
- Building a model using Batch Transform in SageMaker - use the Python SDK to interact with SageMaker
- 1.03_Boston Housing - XGBoost - Low Level
- Building a model using SageMaker
- Low level approach where we describe different tasks we want SageMaker to perform
- The high level approach makes developing new models very straightforward, requiring very little code. The reason this can be done is that certain decisions have been made for you.
- The low level approach allows you to be far more particular in how you want the various tasks executed, which is good for when you want to do something a little more complicated.
- 1.04_Boston Housing - XGBoost (Deploy) - High Level
- Building and Deploying simple model using the Python SDK to interact with SageMaker
- 1.05_Boston Housing - XGBoost (Deploy) - Low Level
- Building and Deploying simple model with the low level approach
- Using the low level approach to deploy our model requires us to create an endpoint, which will be used to send data to our model and to get inference results.
- In order to create an endpoint in SageMaker, we first need to describe an endpoint configuration.
- 1.06_IMDB Sentiment Analysis - XGBoost - Web App
- Deploy the sentiment model via a web app that
- Using Amazon Lambda and API Gateway to solve: a) the authentication issue, b) bag of words encoding expected by model vs the block of text in the web app
- Flow is as following: App <<-->> API & Lamda <<-->> Model
- App finished product: https://derekspublicbucket.s3.us-east-2.amazonaws.com/index_sentiment.html
- This app only works when I turn on the model & endpoint in AWS Sagemaker