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
