Practicing Trustworthy Machine Learning

Trusted ML Systems - The Practical Guide to Building Secure, Robust, and Explainable Models Are you tired of sifting through endless theories and concepts in books on AI trustworthiness? Look no further! This practical guide provides a comprehensive blueprint for building industry-grade trusted ML systems.

With the increasing use of AI in high-stakes domains, it's crucial to ensure your models are secure, more robust, less biased, and more explainable.

Our authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices into actionable steps for engineers and data scientists. Learn how to: * Explain ML models and their outputs to stakeholders * Recognize and fix fairness concerns and privacy leaks in an ML pipeline * Develop ML systems that are robust and secure against malicious attacks * Manage trust debt and identify which ML obstacles require human intervention This book is a must-read for anyone looking to build trustworthy ML applications.

With code examples and practical advice, you'll be able to release your models into a noisy, messy, and often hostile world with confidence.

Don't miss out on this opportunity to stay ahead of the curve in the rapidly evolving field of AI trustworthiness.

Get your copy today!