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Название: Reliable Machine Learning: Applying SRE Principles to ML in Production (Early Release)
Автор: Cathy Chen, Kranti Parisa, Niall Richard Murphy
Издательство: O’Reilly Media, Inc.
Год: 2021-10-12
Страниц: 93
Язык: английский
Формат: epub
Размер: 10.2 MB
Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. Most of this book is about managing machine learning systems and production level ML pipelines. This involves work that is quite different from the work often performed by many data scientists and machine learning researchers, who ideally spend their days trying to develop new predictive models and methods that can squeeze out another percentage point of accuracy. Instead, in this book, we focus on ensuring that a system that includes an ML model exhibits consistent, robust, and reliable system level behavior.
Автор: Cathy Chen, Kranti Parisa, Niall Richard Murphy
Издательство: O’Reilly Media, Inc.
Год: 2021-10-12
Страниц: 93
Язык: английский
Формат: epub
Размер: 10.2 MB
Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. Most of this book is about managing machine learning systems and production level ML pipelines. This involves work that is quite different from the work often performed by many data scientists and machine learning researchers, who ideally spend their days trying to develop new predictive models and methods that can squeeze out another percentage point of accuracy. Instead, in this book, we focus on ensuring that a system that includes an ML model exhibits consistent, robust, and reliable system level behavior.