Feature Store for Machine Learning :: Curate, Discover, Share and Serve ML Features at Scale.
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...
Gespeichert in:
1. Verfasser: | |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2022.
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Schlagworte: | |
Online-Zugang: | Volltext |
Zusammenfassung: | 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. |
Beschreibung: | 1 online resource (281 pages) |
ISBN: | 1803245980 9781803245980 |
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520 | |a 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. | ||
505 | 0 | |a 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. | |
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776 | 0 | 8 | |i Print version: |a J, Jayanth Kumar M. |t Feature Store for Machine Learning. |d Birmingham : Packt Publishing, Limited, ©2022 |
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author | Kumar M. J., Jayanth |
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contents | 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. |
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spelling | Kumar M. J., Jayanth. Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. Birmingham : Packt Publishing, Limited, 2022. 1 online resource (281 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. 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 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Machine learning fast Print version: J, Jayanth Kumar M. Feature Store for Machine Learning. Birmingham : Packt Publishing, Limited, ©2022 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3296295 Volltext |
spellingShingle | Kumar M. J., Jayanth Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. 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. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Machine learning fast |
subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 |
title | Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. |
title_auth | Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. |
title_exact_search | Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. |
title_full | Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. |
title_fullStr | Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. |
title_full_unstemmed | Feature Store for Machine Learning : Curate, Discover, Share and Serve ML Features at Scale. |
title_short | Feature Store for Machine Learning : |
title_sort | feature store for machine learning curate discover share and serve ml features at scale |
title_sub | Curate, Discover, Share and Serve ML Features at Scale. |
topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Apprentissage automatique. Machine learning fast |
topic_facet | Machine learning. Apprentissage automatique. Machine learning |
url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3296295 |
work_keys_str_mv | AT kumarmjjayanth featurestoreformachinelearningcuratediscovershareandservemlfeaturesatscale |