[mmierz]

I'm currently working in an MLOps engineer role at a mid sized company. I agree with that article that most of what I do is plain old software engineering. I don't think I'm interchangeable with any other backend dev though, because ML expertise really does come in handy here. I think the thing that makes it a bit specialized is that we are providing tools to allow our data scientists to self-serve model deployment and monitoring, but they by and large not expert web programmers. So we need to anticipate the kind of mistakes they're likely to make and provide opinionated tools that guide them into building sane software in the specific context of our company's technology. As well as direct support as needed.

We evaluated several commercial MLops tools and ended up going with generic tools that we already use, instead of something new that's branded for MLops. I.e. postgres + snowflake instead of a commercial feature store -- model deployment, monitoring, and alerting on the same platform as the rest of the company's applications -- etc. When we tried "ML" tools, they took so much work to adapt to our use cases that they really added no value.