Reliable machine learning : applying SRE principles to ML in production / Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley & Todd Underwood ; foreword by Sam Charrington.

By:

Chen, Cathy [author.]

Contributor(s):

Material type: Text

TextPublisher: Beijing Sebastopol, CA : O'Reilly, 2022Description: xxix, 376 pages ; 24 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9781098106225

Subject(s):

DDC classification:

670.285631 23
006.31 CHE 23/eng/20220928 24721

LOC classification:

Q325.5 .C435 2022

Summary: 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.

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Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	COMSATS University Wah Campus	006.31 CHE 24721 (Browse shelf(Opens below))	Available		10004000024721

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Previous	006.31 ALP 23796 Introduction to machine learning	006.31 ALP 23797 Introduction to machine learning	006.31 ALP 23798 Introduction to machine learning	006.31 CHE 24721 Reliable machine learning : applying SRE principles to ML in production /	006.31 DRO 24688 The science of deep learning /	006.31 DUT 23613 Machine learning	006.31 GAL 24732 Machine learning with Python cookbook : practical solutions from preprocessing to deep learning /	Next

Includes bibliographical references and index.

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.

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