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 guests show you how to run an efficient 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

Effective "productionization," and how it can be made easily monitorable, deployable, and operable

Why ML systems make production troubleshooting more difficult, and how to get around them

How ML, product, and production teams can communicate effectively

More Information
ISBN/EAN 9781098106225
Author Cathy Chen, Niall Richard Murphy, Kranti Parisa, D Sculley, Todd Underwood
Publisher O'Reilly Media
Publication date 30 Sep 2022
Format Paperback
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You're reviewing:Reliable Machine Learning : Applying SRE Principles to ML in Production

Reliable Machine Learning : Applying SRE Principles to ML in Production

Cathy Chen, Niall Richard Murphy, Kranti Parisa, D Sculley, Todd Underwood
€72.99

Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization. 

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