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portada Practical Mlops: Operationalizing Machine Learning Models
Type
Physical Book
Author
Publisher
Year
2021
Language
English
Pages
458
Format
Paperback
Dimensions
23.3 x 17.8 x 2.4 cm
Weight
0.73 kg.
ISBN13
9781098103019
Edition No.
1
Categories

Practical Mlops: Operationalizing Machine Learning Models

Noah Gift (Author) · Alfredo Deza (Author) · O'Reilly Media · Paperback

Practical Mlops: Operationalizing Machine Learning Models - Gift, Noah ; Deza, Alfredo

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Synopsis "Practical Mlops: Operationalizing Machine Learning Models "

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 learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

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