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

Importance of features in production

Before discussing how to bring features to production, let's understand why features are needed in production. Let's go through an example.

We often use taxi and food delivery services. One of the good things about these services is that they tell us how long it will take for our taxi or food to arrive. Also, most of the time, it is approximately correct. How does it predict this accurately? It uses ML, of course. The ML model predicts how long it will take for the taxi or food to arrive. For a model like that to be successful, not only does it need a good feature engineering and ML algorithm, but also the most recent features. Though we don't know the exact feature set that the model uses, let's look at a couple of features that change dynamically and are very important.

With food delivery services, the major components that affect the delivery time are restaurants, drivers, traffic, and customers. The model probably...