Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines
Pruksachatkun, Yada; Mcateer, Matthew; Majumdar, Subho
Language: English
Published by O'Reilly Media, 2023
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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In English.
Seller Inventory # ria9781098120276_new
- Title
- Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines
- Author
- Pruksachatkun, Yada; Mcateer, Matthew; Majumdar, Subho
- Publisher
- O'Reilly Media
- Publication year
- 2023
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1098120272
- ISBN 13
- 9781098120276
- Item weight
- 666 grams
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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
Ria Christie Collections
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