Description: Practicing Trustworthy Machine Learning : Consistent, Transparent, and Fair Ai Pipelines, Paperback by Pruksachatkun, Yada; Mcateer, Matthew; Majumdar, Subho, ISBN 1098120272, ISBN-13 9781098120276, Brand New, Free shipping in the US 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. You'll learn: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
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Book Title: Practicing Trustworthy Machine Learning : Consistent, Transparent
Number of Pages: 300 Pages
Publication Name: Practicing Trustworthy Machine Learning : Consistent, Transparent, and Fair Ai Pipelines
Language: English
Publisher: O'reilly Media, Incorporated
Publication Year: 2023
Item Height: 0.6 in
Subject: Data Modeling & Design, Business Ethics, General
Item Weight: 18.8 Oz
Type: Textbook
Item Length: 9.2 in
Subject Area: Computers, Science, Business & Economics
Author: Yada Pruksachatkun, Matthew Mcateer, Subhabrata Majumdar
Item Width: 7 in
Format: Trade Paperback