Publicado por O'Reilly Media (edition 1), 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
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Añadir al carritoPaperback. Condición: New. 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.
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Añadir al carritoCondición: new. 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, cofounder 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 problem; automating the process for continually developing, evaluating, deploying, and updating models; developing a monitoring system to quickly detect and address issues your models might encounter in production; architecting an ML platform that serves across use cases; and developing responsible ML systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Publicado por O'Reilly Media, Sebastopol, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
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Añadir al carritoPaperback. Condición: new. Paperback. 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 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Añadir al carritoPaperback. Condición: New. 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.
Publicado por O'Reilly Media 6/21/2022, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
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Añadir al carritoPaperback or Softback. Condición: New. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. Book.
Publicado por American Orthopsychiatric Association, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
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Publicado por American Orthopsychiatric Association, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
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Publicado por American Orthopsychiatric Association, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
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Publicado por Oreilly & Associates Inc, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 350 pages. 9.19x7.00x0.80 inches. In Stock.
Publicado por O'Reilly Media, Sebastopol, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
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Añadir al carritoPaperback. Condición: new. Paperback. 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 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por O'reilly Media Jul 2022, 2022
ISBN 10: 1098107969 ISBN 13: 9781098107963
Idioma: Inglés
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware -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. 367 pp. Englisch.