MLOps Engineering at Scale

Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers.   Cloud Native Machine Learning  helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system's infrastructure. Following a real-world use case for calculating taxi fares, you'll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware.

ISBN:	9781617297762
Publisher:	Pearson Education
Imprint:	Manning Publications
Published date:	16 Mar 2022
DEWEY:	006.31
DEWEY edition:	23
Language:	English
Number of pages:	xxii, 319
Weight:	628g
Height:	187mm
Width:	234mm
Spine width:	24mm

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