Learn how to leverage feature stores to make the most of your machine learning models
Purchase of the print or Kindle book includes a free PDF eBook
What’s inside
- Insights into the significance of feature stores in the ML life cycle
- Easy-to-follow techniques to show you how features can be shared, discovered, and re-used
- Instructions to make features available for online models during inference
You’ll get the most out of this book if
- You have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them
- You’re a data/machine learning engineer or a data scientist who builds machine learning models for production systems in any domain
- Your role includes supporting data engineers in productionizing ML models
- You're a platform engineer who builds data science (ML) platforms for your organization
What your journey will look like
This book will show you how to utilize feature stores to their fullest potential, saving you a lot of time and effort by teaching you 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.
Some of the things you’ll learn from this book
- The significance of feature stores in a machine learning pipeline
- How to curate, store, share and discover features using feature stores
- The different components and capabilities of a feature store
- How to use feature stores with batch and online models
- Accelerating your model life cycle and reducing costs
- Effective deployment of your first feature store for production use cases
Table of Contents
- An Overview of the Machine Learning Life Cycle
- What Problems Do Feature Stores Solve?
- Feature Store Fundamentals, Terminology, and Usage
- Adding Feature Store to ML Models
- Model Training and Inference
- Model to Production and Beyond
- Feast Alternatives and ML Best Practices
- Use Case – Customer Churn Prediction
Jayanth Kumar M J is a Lead Data engineer at Cimpress USA. He specializes in building platform components for data scientists and data engineers to make MLops smooth and self-service. He is also a Feast feature store contributor.
