Practitioners guide to MLOps: A framework for continuous ...
Practitioners guide to MLOps: A framework for continuous delivery and automation of machine paperMay 2021Authors: Khalid Salama, Jarek Kazmierczak, Donna SchutTable of ContentsExecutive summary 3Overview of MLOps lifecycle and core capabilities 4Deep dive of MLOps processes 15Putting 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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