Niall Richard Murphy

Cathy Chen

Kranti Parisa

D. Sculley

Todd Underwood

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

Reliable Machine Learning

Matt Mitchell

Eric Pugh

David Smiley

This book is for developers who want to learn how to get the most out of Solr in their applications, whether you are new to the field, have used Solr but don't know everything, or simply want a good reference. It would be helpful to have some familiarity with basic programming concepts, but no prior experience is required.

Apache Solr Enterprise Search Server - Third Edition

Kranti Parisa

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