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

Common problems with the approaches used for bringing features to production

The approaches discussed in the previous section seem like good solutions. However, not only does every approach have its own technical difficulties, such as infrastructure sizing, keeping up with a service-level agreement (SLA), and interaction with different systems, but they have a few common problems as well. This is expected in a growing technical domain until it reaches a level of saturation. I want to dedicate this section to the common problems that exist in these approaches.

Re-inventing the wheel

One of the common problems in engineering is building something that already exists. The reasons for that could be many; for example, a person developing a solution may not know that it already exists, or the existing solution is inefficient, or there is a need for additional functionality. We have the same problem here.

In many organizations, data scientists work in a specific domain and with...