Practical MLOps: Operationalizing Machine Learning Models


Practical MLOps: Operationalizing Machine Learning Models
Authors: Noah Gift
Year: 2021
Publisher: O’Reilly Media
Language: English
ISBN 13: 9781098103019
ISBN 10: 1098103017
Categories: Computers, Computer Science
Pages: 320 / 319
Edition: 1

Availability: 5000 in stock

SKU: 9781098103019 Categories: ,

Practical MLOps: Operationalizing Machine Learning Models Noah Gift, Alfredo Deza
Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers–or anyone familiar with data science and Python–will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you’re trying to crack. This book gives you a head start.You’ll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware Categories:
Computers – Computer Science
O’Reilly Media
46 461
ISBN 10:
ISBN 13:
732 MB


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Practical MLOps: Operationalizing Machine Learning Models

Availability: 5000 in stock