{"id":21455,"date":"2024-08-24T10:21:32","date_gmt":"2024-08-24T10:21:32","guid":{"rendered":"https:\/\/goglobalshopping.online\/index.php\/2024\/08\/24\/practicing-trustworthy-machine-learning-consistent-transparent-and-fair-ai-pipelines-grayscale-indian-edition\/"},"modified":"2024-08-24T10:21:32","modified_gmt":"2024-08-24T10:21:32","slug":"practicing-trustworthy-machine-learning-consistent-transparent-and-fair-ai-pipelines-grayscale-indian-edition","status":"publish","type":"post","link":"https:\/\/goglobalshopping.online\/index.php\/2024\/08\/24\/practicing-trustworthy-machine-learning-consistent-transparent-and-fair-ai-pipelines-grayscale-indian-edition\/","title":{"rendered":"Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines (Grayscale Indian Edition)"},"content":{"rendered":"


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Disclaimer<\/p>\n

Shroff Publishers do not endorse the preview pages of kindle linked to our ISBNs. All Indian Reprints of O’Reilly are Printed in Grayscale.<\/p>\n

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.<\/p>\n

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.<\/p>\n

You’ll learn:<\/p>\n

Methods to explain ML models and their outputs to stakeholdersHow to recognize and fix fairness concerns and privacy leaks in an ML pipelineHow to develop ML systems that are robust and secure against malicious attacksImportant systemic considerations, like how to manage trust debt and which ML obstacles require human intervention <\/p>\n

Publisher \u200f : \u200e Shroff\/O’Reilly; First Edition (18 January 2023); Shroff Publishers & Distributors Pvt. Ltd., B-103, First Floor, Railway Commercial Complex, Sector 3, Sanpada (E), Navi Mumbai 400705, WhatsApp: +917304487700, email: info@shroffpublishers.com
Paperback \u200f : \u200e 304 pages
ISBN-10 \u200f : \u200e 9355422199
ISBN-13 \u200f : \u200e 978-9355422194
Reading age \u200f : \u200e 10 years and up
Item Weight \u200f : \u200e 470 g
Dimensions \u200f : \u200e 22.86 x 17.78 x 1.52 cm
Country of Origin \u200f : \u200e India
Packer \u200f : \u200e Shroff Publishers & Distributors Pvt. Ltd., B-103, First Floor, Railway Commercial Complex, Sector 3, Sanpada (E), Navi Mumbai 400705, WhatsApp: +917304487700, email: info@shroffpublishers.com <\/p>\n

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