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.

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Spis treści

Feature Store for Machine Learning. Curate, discover, share and serve ML features at scale eBook -- spis treści

• 1. An Overview of the Machine Learning Life Cycle
• 2. What Problems Do Feature Stores Solve?
• 3. Feature Store Fundamentals, Terminology, and Usage
• 4. Adding Feature Store to ML Models
• 5. Model Training and Inference
• 6. Model to Production and Beyond
• 7. Feast Alternatives and ML Best Practices
• 8. Use Case – Customer Churn Prediction