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Synopsis
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.
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About the Authors
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.
Matthew McAteer works on machine learning at Formic Labs, a startup focused on in silico cell simulation. He is also the creator of 5cube Labs, an ML consultancy that has worked with over 100 companies in industries ranging from architecture to medicine to agriculture. Matthew previously worked with the TensorFlow team at Google on probabilistic programming, and with the general-purpose AI research company Generally Intelligent. Before he was an ML engineer, Matthew worked in biomedical research labs at MIT, Harvard Medical School, and Brown University.
Subhabrata (Subho) Majumdar is a Senior Applied Scientist at Splunk. Previously, he spent 3 years in AT&T, where he led research and development on ethical AI. Subho deeply believes in the power of data to bring about positive changes in the world---he has cofounded the Trustworthy ML Initiative, and has been a part of multiple successful industry-academia collaborations in the data for good space. Subho holds a PhD in Statistics from the University of Minnesota.
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Paperback. Condition: New. 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. Seller Inventory # LU-9781098120276
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