Feature Store for Machine Learning
- Paperback: 280 pages
- Publisher: WOW! eBook (June 30, 2022)
- Language: English
- ISBN-10: 1803230061
- ISBN-13: 978-1803230061
Feature Store for Machine Learning: Learn how to leverage feature stores to make the most of your machine learning models
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.
- Understand the significance of feature stores in a machine learning pipeline
- Become well-versed with how to curate, store, share and discover features using feature stores
- Explore the different components and capabilities of a feature store
- Discover how to use feature stores with batch and online models
- Accelerate your model life cycle and reduce costs
- Deploy your first feature store for production use cases
By the end of this Feature Store for Machine Learning 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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