Reliable machine learning: applying sre principles to ml in production, cathy chen,...
Описание
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
Також купити книгу Reliable Machine Learning: Applying SRE Principles to ML in
Production, Cathy Chen, Niall Murphy, Kranti Parisa, D. Sculley, Todd
Underwood, more Ви можете по посиланню
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