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Reliable Machine Learning: Applying Sre Principles to Ml in Production

Reliable Machine Learning: Applying Sre Principles to Ml in Production

Reliable Machine Learning: Applying Sre Principles to Ml in Production
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Reliable Machine Learning: Applying Sre Principles to Ml in Production Paperback - 2022

by Chen, Cathy/ Murphy, Niall/ Parisa, Kranti/ Sculley, D./ Underwood, Todd

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Oreilly & Associates Inc, 2022. Paperback. New. 350 pages. 9.19x7.00x1.00 inches.
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Details

  • Title Reliable Machine Learning: Applying Sre Principles to Ml in Production
  • Author Chen, Cathy/ Murphy, Niall/ Parisa, Kranti/ Sculley, D./ Underwood, Todd
  • Binding Paperback
  • Condition New
  • Pages 408
  • Volumes 1
  • Language ENG
  • Publisher Oreilly & Associates Inc
  • Publication date 2022
  • Illustrated Yes
  • Features Illustrated, Index
  • Bookseller's Inventory # x-1098106229
  • ISBN 9781098106225 / 1098106229
  • Weight 1.5 lbs (0.68 kg)
  • Dimensions 9.1 x 6.9 x 1 in (23.11 x 17.53 x 2.54 cm)
  • Category Computers - Communications / Networking
  • Library of Congress subjects Reliability (Engineering), Production engineering - Data processing
  • Dewey Decimal Code 670.285
  • Quantity available 2

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From the publisher

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

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