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Couldn’t you say it’s just devops applied to a specific type of app that often isn’t deployed to production? And takes significant understanding of the underlying systems to deploy? It certainly feels like ML discovering devops.

That can still mean it requires a unique skillset that devops people dont generally have.

It’s just hard to imagine how infrastructure and deployment suddenly isn’t devops because it deals with ML.

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I mean there has definitely been an explosion of "Ops" terms, and not all of them are justified. I think MLOps makes sense to emphasise the three pieces though. I know people want the "Ops" part of "DevOps" to mean "magic optimization for anything", but it goes back to "ops" as in infrastructure/servers etc. So if DevOps is software engineering + deploying and running software reliably, then it still leaves a big gap on the "data" side - both in terms of orchestrating source data, but also in handling large model files, and tracking things like model decay.

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