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Practicing trustworthy machine learning : = consistent, transparent, and fair AI pipelines /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Practicing trustworthy machine learning :/ Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar.
其他題名:	consistent, transparent, and fair AI pipelines /
其他題名:	Consistent, transparent, and fair artifical intelligence pipelines
作者:	Pruksachatkun, Yada,
其他作者:	McAteer, Matthew,
出版者:	Sebastopol, CA :O'Reilly Media, Inc., : 2023.,
面頁冊數:	xxiv, 274 p. :ill. ; : 23 cm.;
標題:	Machine learning. -
ISBN:	9781098120276 (pbk.) :

Practicing trustworthy machine learning : = consistent, transparent, and fair AI pipelines /
Pruksachatkun, Yada,

Practicing trustworthy machine learning :consistent, transparent, and fair AI pipelines /Consistent, transparent, and fair artifical intelligence pipelinesYada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar. - 1st ed. - Sebastopol, CA :O'Reilly Media, Inc.,2023. - xxiv, 274 p. :ill. ;23 cm.

Includes bibliographic references and index.

Preface --

"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. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world" --

ISBN: 9781098120276 (pbk.) :NT2656

LCCN: 2023278332

Nat. Bib. No.: GBC307044bnb

Nat. Bib. Agency Control No.: 020868348UkSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .P78 2023

Dewey Class. No.: 006.3/1

Practicing trustworthy machine learning : = consistent, transparent, and fair AI pipelines /
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504 $a Includes bibliographic references and index.
505 0 0 $t Preface -- $t Privacy -- $t Fairness and bias -- $t Model explainability and interpretability -- $t Robustness -- $t Secure and trustworthy data generation -- $t More state-of-the-art research questions -- $t From theory to practice -- $t An ecosystem of trust -- $t Synthetic data generation tools -- $t Other interpretability and explainability tool kits -- $t Index -- $t About the authors.
520 # $a "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. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world" -- $c Provided by publisher.
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