Practicing Trustworthy Machine Learning 2nd Early Release

Practicing Trustworthy Machine Learning (Second Early Release) Yada Pruksachatkun, Matthew McAteer, Subhabrata Majumdar
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 stakeholders
How to recognize and fix fairness concerns and privacy leaks in an ML pipeline
How to develop ML systems that are robust and secure against malicious attacks
Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention Categories:
Computers – Artificial Intelligence (AI)
Year:
2022
Edition:
2022-06-28: Second Early Release
Publisher:
O’Reilly Media
Language:
english
Pages:
239
ISBN 10:
1098120264
ISBN 13:
9781098120269
File:
106 MB