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Practitioners guide to MLOps: A framework for continuous ...

Practitioners guide to MLOps: A framework for continuous delivery and automation of machine paperMay 2021 Authors: Khalid Salama, Jarek Kazmierczak, Donna SchutTable of ContentsExecutive summary 3 Overview of MLOps lifecycle and core capabilities 4 Deep dive of MLOps processes 15 Putting it all together 34 Additional resources 36 Building an ML-enabled system

ML models are built and deployed in production using curated data that is usually created by the data engineering team. The models do not operate in silos; they are components of, and support, a large range of application systems, such as business intelligence systems, line of business applications, process control systems, and embedded sys-tems.

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