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This document sets out the current state of MLOps and provides a five year roadmap for future customer needs which is intended to support pre-competitive collaboration across the industry with a view to improving the overall state of MLOps as a capability for all. It is intended that this document be iteratively refined by group consensus within the MLOps SIG across a series of regular meetings and then published annually whilst relevant. Acknowledgements Current active contributors to the MLOps SIG Roadmap: Terry Cox, Bootstrap Ltd terry@bootstrap.je Michael Neale, CloudBees michael.neale@gmail.com Kara de la Marck, CDF karadelamarck@gmail.com Ian Hellström, D2IQ Almog Baku, Rimoto almog.baku@gmail.com Eric Peter eric@ericpeter.me Update publication - Nov 1st, 2022 MLOps could be narrowly defined as "the ability to apply DevOps principles to Machine Learning applications" however as we shall see shortly, this narrow definition misses the true value of MLOps to the customer. Instead, w

MLOps Roadmap 2022 Warning This document is now outdated. Please see the 2024 edition About this document This document sets out the current state of MLOps and provides a five year roadmap for future customer needs which is intended to support pre-competitive collaboration across the industry with a view to improving the overall state of MLOps as a capability for all. It is intended that this document be iteratively refined by group consensus within the MLOps SIG across a series of regular meetings and then published annually whilst relevant. Acknowledgements Current active contributors to the

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