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Название: Practicing Trustworthy Machine Learning: Consistent, Transparent, and Safe AI Pipelines (Second Early Release)
Автор: Yada Pruksachatkun, Matthew McAteer, Subhabrata Majumdar
Издательство: O’Reilly Media, Inc.
Год: 2022-10-14
Страниц: 313
Язык: английский
Формат: epub (true), mobi
Размер: 47.3 MB
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. This book is written for anyone who is currently working with Machine Learning models and wants to be sure that the fruits of their labor will not cause unintended harm when released into the real world. The primary audience of the book are engineers and data scientists who have some familiarity with Machine Learning. Parts of the book should be accessible to non-engineers, such as product managers and executives with a conceptual understanding of ML. Some of you may be building ML systems that make higher-stakes decisions than they encountered in your previous job or in academia. We assume you are are familiar with the very basics of deep learning, and Python for the code samples.
Автор: Yada Pruksachatkun, Matthew McAteer, Subhabrata Majumdar
Издательство: O’Reilly Media, Inc.
Год: 2022-10-14
Страниц: 313
Язык: английский
Формат: epub (true), mobi
Размер: 47.3 MB
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. This book is written for anyone who is currently working with Machine Learning models and wants to be sure that the fruits of their labor will not cause unintended harm when released into the real world. The primary audience of the book are engineers and data scientists who have some familiarity with Machine Learning. Parts of the book should be accessible to non-engineers, such as product managers and executives with a conceptual understanding of ML. Some of you may be building ML systems that make higher-stakes decisions than they encountered in your previous job or in academia. We assume you are are familiar with the very basics of deep learning, and Python for the code samples.