Reliable Machine Learning

by Cathy Chen, Niall Richard Murphy, Kranti Parisa

Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.

By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.

You'll examine:


What ML is: how it functions and what it relies on
Conceptual frameworks for understanding how ML loops work
How effective productionization can make your ML systems easily monitorable, deployable, and operable
Why ML systems make production troubleshooting more difficult, and how to compensate accordingly
How ML, product, and production teams can communicate effectively

Genres: Artificial Intelligence, Technology, Technical

Reviews

[Thang · Rating 3 out of 5]

This book is for organization managers rather than data scientists or ML engineers. It covers the basic understanding of ML development, continuous ML requirements, and incident handling.
Some contents are repetitive and do not have much new information.

[yacoob · Rating 4 out of 5]

Excellent overview of challenges specific to systems employing machine learning algorithms. There's a bit of overlap between chapters, as a result of different authors contributing them - that's the only reason I've not given it 5/5.