num_pages: 458
description: 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 
language: English
title: Practical MLOps: Operationalizing Machine Learning Models
publisher: O'Reilly Media