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I agree there's a lot of "Agile" like BS around MLOps, but this article doesn't really give the prior art enough attention IMO. Data Engineering is a large part

I agree there's a lot of "Agile" like BS around MLOps, but this article doesn't really give the prior art enough attention IMO. Data Engineering is a large part of MLOps, but there are unique parts of production ML engineering so it makes sense that it is (slowly) evolving its own discipline. There are some people who have expertise in building production infrastructure, writing production code, and managing production data, but they are few and far between. So finding a "system" that lets data experts work with code experts work with infrastructure experts is important. Many people say it's "just software engineering" or "just DevOps", but I feel like they are either not respective enough of the challenges of whichever pillar they are ignoring, or they don't even know that those challenges exist. Filtering out the BS and finding the smart people who are writing interesting things about MLOps is difficult as they use the same terminology (and if the smart people switched, the BS people would follow, so they may as well stand their ground) but the BS cover doesn't mean that there's nothing substantial underneath.

nonethewiser 4y ago
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.
- ritzaco 4y ago
  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.

nonethewiser 4y ago

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.

ritzaco 4y ago
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.