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.