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Practical MLOps : Operationalizing Machine Learning Models by Alfredo Deza and Noah Gift (2021, Trade Paperback)
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Dive into the world of machine learning operations with "Practical MLOps PAPERBACK," authored by Alfredo Deza and Noah Gift. Published by O'Reilly Media in 2021, this comprehensive guide offers insights into operationalizing machine learning models in a variety of settings. With 458 pages of in-depth content, this trade paperback format is perfect for both on-the-go reading and in-depth study.Explore a range of topics pertinent to the computer science and business intelligence communities. The book delves into the nuances of MLOps, addressing subject areas that include intelligence (AI) and semantics. Sized at 9.2 x 7.2 inches with a comfortable 1.1-inch spine, this textbook is a valuable resource for both seasoned professionals and students new to the field.
- Buy It NowPractical MLOps: Operationalizing Machine Learning Models, Gift, Noah,Deza, Alfr
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101098103017
ISBN-139781098103019
eBay Product ID (ePID)15050089630
Product Key Features
Number of Pages450 Pages
Publication NamePractical Mlops : Operationalizing Machine Learning Models
LanguageEnglish
Publication Year2021
SubjectEnterprise Applications / Business Intelligence Tools, Intelligence (Ai) & Semantics, General
TypeTextbook
Subject AreaComputers, Science
AuthorAlfredo Deza, Noah Gift
FormatTrade Paperback
Dimensions
Item Height1 in
Item Weight28 Oz
Item Length9.2 in
Item Width7.2 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2022-300025
Dewey Edition23
IllustratedYes
Dewey Decimal006.3/1
SynopsisGetting 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
LC Classification NumberQ325.5
