Feature Store for Machine Learning

Chapter 5: Model Training and Inference

In the last chapter, we discussed Feast deployment in the AWS cloud and set up S3 as an offline store and DynamoDB as an online store for the model. We also revisited the few stages of the ML life cycle using the Customer Lifetime Value (LTV/CLTV) model built in Chapter 1, An Overview of the Machine Learning Life Cycle. During the processing of model development, we performed data cleaning and feature engineering and produced the feature set for which the feature definitions were created and applied to Feast. In the end, we ingested the features into Feast successfully and we were also able to query the ingested data.

In this chapter, we will continue with the rest of the ML life cycle, which will involve ...