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Practical Mlops: Operationalizing Machine Learning Models by Gift, Noah

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 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

Price: 48.27 GBP

Location: Gloucester

End Time: 2024-11-02T10:03:56.000Z

Shipping Cost: 22.99 GBP

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Practical Mlops: Operationalizing Machine Learning Models by Gift, NoahPractical Mlops: Operationalizing Machine Learning Models by Gift, Noah

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EAN: 9781098103019

UPC: 9781098103019

ISBN: 9781098103019

MPN: N/A

Book Title: Practical Mlops: Operationalizing Machine Learning

Item Length: 23.3 cm

Item Weight: 0.47 kg

Item Height: 232 mm

Item Width: 178 mm

Author: Alfredo Deza, Noah Gift

Publication Name: Practical Mlops: Operationalizing Machine Learning Models

Format: Paperback

Language: English

Publisher: O'reilly Media, INC International Concepts USA

Subject: Computer Science

Publication Year: 2021

Type: Textbook

Number of Pages: 450 Pages

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