From Serverless Machine Learning in Action by Carl Osipov

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Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Serverless Machine Learning in Action 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.