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

Handling changes to the feature set during development

Model development is an evolving process. So are models – they evolve over time. Today, we may be using a few features for a specific model, but as and when we discover and try out new features, if the performance is better than the current model, we might end up including the new features in the model training and scoring. Hence, the feature set may change over time. What that means with the feature store is some of the steps we performed in Chapter 4, Adding Feature Store to ML Models, might need to be revisited. Let's look at what those steps are.

Important Note

The assumption here is feature definitions change during model development, not after production. We will look at how to handle changes to the feature set after the model goes into production in later chapters.

Step 1 – Change feature definitions

If the features or entities change during the model development, the first step is to update...