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

Philosophy behind feature stores

In this chapter, we have discussed different issues with ML pipelines and how feature stores help data scientists solve them and accelerate ML development. In this section, let's try to understand the philosophy behind feature stores and try to make sense of why having a feature store in our ML pipeline may be the ideal way to accelerate ML. Let's start with a real-world example as we are trying to build real-world experience with ML. You will be given the names of two phones; your job is to figure out which one is better. The names are iPhone 13 Pro and Google Pixel 6 Pro. You have an infinite amount of time to find the answer; continue reading once you have the answer.

As Ralph Waldo Emerson said, It's not the destination, it is the journey. Whatever your answer may be, however long you took to arrive at it, let's look at how you might have arrived at it. Some of you might have got an answer right away, but if you haven&apos...