Using a feature store

A feature store is a software layer on top of your data to abstract all the production and management processes for data by providing inference systems with an interface to retrieve a feature set that can be used for inference or training.

In this section, we will illustrate the concept of a feature store by using Feast (a feature store), an operational data system for managing and serving machine learning features to models in production:

Figure 7.8 – Feast Architecture (retrieved from https://docs.feast.dev/)

In order to understand how Feast works and how it can fit into your data layer component (code available at https://github.com/PacktPublishing/Machine-Learning-Engineering-with-MLflow/tree/master/Chapter07/psystock_feature_store, execute the following steps:

1. Install feast:

   pip install feast==0.10

2. Initialize a feature repository:

   feast init

3. Create your feature definitions by replacing the yaml file generated...