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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

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 changes in the data, changes in the model, users trying to game the system, and so on. This is what MLOps is about.

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