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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

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Marque : GENERIC
Vendu par HEAVENBOOKS.MA

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, ma...

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Description produit
Marque
GENERIC
Titre principal
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
Editeur
O'Reilly Media
Type de produit
Paperback
Présentation du livre
paperback
Release date
6/21/2022 12:00:00 AM
Langue d'origine
English
ISBN
1098107969
Dimensions
6.9 x 0.7 x 9.1 inches
Nombre de pages de livre
386 pages
Langue - Librairie
English
Résumé
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problemAutomating the process for continually developing, evaluating, deploying, and updating modelsDeveloping a monitoring system to quickly detect and address issues your models might encounter in productionArchitecting an ML platform that serves across use casesDeveloping responsible ML systems Read more
Auteur(s)
Chip Huyen
Date de parution
6/21/2022 12:00:00 AM