Scherpste prijs
Kenmerken
Auteur
Alfredo Deza
Conditie
Zo goed als nieuw
Productnummer (ISBN)
9781098103019
Jaar (oorspr.)
2021
Beschrijving
Gratis verzending. Voor 15.00 besteld? Vandaag verzonden!
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Titel: Practical MLOps
Auteur: Alfredo Deza
ISBN: 9781098103019
Conditie: Als nieuw
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
BoekenBalie maakt van tweedehands jouw eerste keuze. Met een Trustscore van 4,8 (excellent) en 30 dagen retour garantie maken we dat iedere dag waar.
Bestel direct op onze website!
Titel: Practical MLOps
Auteur: Alfredo Deza
ISBN: 9781098103019
Conditie: Als nieuw
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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Sinds 27 nov '25
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