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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

MLOps supports ML development and deployment in the way that DevOps and DataOps support application engi-neering and data engineering (analytics). The difference is that when you deploy a web service, you care about resil-ience, queries per second, load balancing, and so on. When you deploy an ML model, you also need to worry about

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