Feature Store for Machine Learning

Chapter 4: Adding Feature Store to ML Models

In the last chapter, we discussed Feast installation in your local system, common terminology in Feast, what the project structure looks like, API usage with an example, and a brief overview of the Feast architecture.

So far in the book, we have been talking about issues with feature management and how a feature store can benefit data scientists and data engineers. It is time for us to get our hands dirty with an ML model and add Feast to the ML pipeline.

In this chapter, we will revisit the Customer Lifetime Value (LTV/CLTV) ML model built in Chapter 1, An Overview of the Machine Learning Life Cycle. We will use AWS cloud services instead of the local system to run the examples in this chapter. ...