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O'Reilly Practical MLOps - Operationalizing ML Models Book

Amazon

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Description

Getting machine learning models into production is a fundamental challenge. Practical MLOps by O'Reilly provides a set of proven principles designed to solve this problem through reliable and automated methods. This guide explains the core concepts of MLOps and how it differs from standard DevOps practices. Designed for current and aspiring machine learning engineers, as well as those familiar with Python and data science, this book helps you build a strong foundation. You will learn to use MLOps tools and methods, including AutoML, monitoring, and logging, to successfully operationalize your machine learning models in real-world environments.

Key Features

Learn the core principles of MLOps to move machine learning models from development into production.

Understand the specific differences between MLOps and DevOps to better manage your workflows.

Build a technical foundation in MLOps tools and methods including AutoML, monitoring, and logging.

This O'Reilly guide is written for machine learning engineers and anyone familiar with Python and data science.

Gain practical knowledge on how to implement automated and reliable processes for machine learning models.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
30 September 2021
Listed Since
30 April 2021

Barcode

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