Book Image

Feature Store for Machine Learning

By : Jayanth Kumar M J
Book Image

Feature Store for Machine Learning

By: Jayanth Kumar M J

Overview of this book

Feature store is one of the storage layers in machine learning (ML) operations, where data scientists and ML engineers can store transformed and curated features for ML models. This makes them available for model training, inference (batch and online), and reuse in other ML pipelines. Knowing how to utilize feature stores to their fullest potential can save you a lot of time and effort, and this book will teach you everything you need to know to get started. Feature Store for Machine Learning is for data scientists who want to learn how to use feature stores to share and reuse each other's work and expertise. You’ll be able to implement practices that help in eliminating reprocessing of data, providing model-reproducible capabilities, and reducing duplication of work, thus improving the time to production of the ML model. While this ML book offers some theoretical groundwork for developers who are just getting to grips with feature stores, there's plenty of practical know-how for those ready to put their knowledge to work. With a hands-on approach to implementation and associated methodologies, you'll get up and running in no time. By the end of this book, you’ll have understood why feature stores are essential and how to use them in your ML projects, both on your local system and on the cloud.
Table of Contents (13 chapters)
1
Section 1 – Why Do We Need a Feature Store?
4
Section 2 – A Feature Store in Action
9
Section 3 – Alternatives, Best Practices, and a Use Case

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 model training, packaging, batch, and online model inference using the feature store. The goal of this chapter is to continue using the feature store infrastructure that was created in the previous chapter and go through the rest of the ML...