Feature Store for Machine Learning : Curate, discover, share and serve ML features at scale

Jayanth Kumar M J

ISBN 10: 1803230061 / ISBN 13: 9781803230061

Verlag: Packt Publishing, 2022

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to leverage feature stores to make the most of your machine learning models Key Features:Understand the significance of feature stores in the ML life cycle Discover how features can be shared, discovered, and re-used Learn to make features available for online models during inference Book Description: 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. What You Will Learn: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 Who this book is for: If you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.

Bestandsnummer des Verkäufers 9781803230061

Bibliografische Details

• Titel: Feature Store for Machine Learning : Curate, discover, share and serve ML features at scale
• Verlag: Packt Publishing
• Erscheinungsjahr: 2022
• Sprache: Englisch
• ISBN-10: 1803230061
• ISBN-13: 9781803230061
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 235B x 191 H x 15 T Millimeter Breite: 235 mm, Höhe: 191 mm, Tiefe: 15 mm
• Gewicht: 528 Gramm

Über diesen Titel

Inhaltsangabe

Learn how to leverage feature stores to make the most of your machine learning models

Key Features

• Understand the significance of feature stores in the ML life cycle
• Discover how features can be shared, discovered, and re-used
• Learn to make features available for online models during inference

Book Description

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.

What you will learn

• 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

Who this book is for

If you have a solid grasp on machine learning basics, but need a comprehensive overview of feature stores to start using them, then this book is for you. Data/machine learning engineers and data scientists who build machine learning models for production systems in any domain, those supporting data engineers in productionizing ML models, and platform engineers who build data science (ML) platforms for the organization will also find plenty of practical advice in the later chapters of this book.

Table of Contents

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

Über die Autorin bzw. den Autor

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.