Reliable Machine Learning

Whether you're a data scientist, software engineer, site reliability engineer, or product manager, this practical guide offers essential insights into establishing and running machine learning systems reliably, effectively, and accountably within any organization. Learn how to implement robust model monitoring in production and cultivate a high-performing model development team. By integrating an SRE mindset with ML practices, this book equips you to optimize decision-making, solve complex problems, and understand customer behavior, ensuring your day-to-day ML tasks align with your strategic objectives.
Why You Should Read?
Understand the fundamental workings and dependencies of machine learning systems.
Grasp conceptual frameworks for effective ML "loops" and their operational implications.
Learn how to make your ML systems easily monitorable, deployable, and operable through effective productionization.
Discover strategies to mitigate troubleshooting difficulties inherent in ML systems and foster better communication across ML, product, and production teams.
About the Author
The authors,
Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, and Todd Underwood
, along with featured guest authors, are distinguished engineering professionals with extensive experience in machine learning and site reliability engineering. Their collective expertise spans leading roles in major technology companies, where they have been instrumental in building and scaling reliable ML systems. This book distills their practical knowledge and best practices, offering readers a unique blend of theoretical understanding and actionable strategies for implementing robust and accountable machine learning solutions in real-world environments.