Yada Pruksachatkun, Matthew Mcateer, Subho Majumdar

Practicing Trustworthy Machine Learning (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: O'Reilly Media
• Seitenzahl: 302
• Erscheinungstermin: 3. Januar 2023
• Englisch
• ISBN-13: 9781098120238
• Artikelnr.: 72455449

Autorenporträt

Yada Pruksachatkun is a machine learning scientist at Infinitus, a conversational AI startup that automates calls in the healthcare system. She has worked on trustworthy natural language processing as an Applied Scientist at Amazon, and led the first healthcare NLP initiative within mid-sized startup ASAPP. She did research transfer learning in NLP in graduate school at NYU and was advised by Professor Sam Bowman.