Is there an ML engineering practice that isn't focused on building models but more on managing/deploying/scaling models? i.e. can I avoid learning all the maths underneath?
I transitioned to this in 2018. It was called MLOps. I was a mobile developer before that. Transitioning was pretty easy at that time (it might be more competitive now). What I did: worked on an intensive ML project for myself and realized I enjoyed working across the ML stack. I wrote a blog post on the project here: https://www.nicksypteras.com/blog/aisu.html. Then I applied to an MLOps team and leveraged the project to demonstrate skills/experience. As for avoiding the math, you could probably get away with it but learning the basics will make everything way easier. I feel like without some basic ML math under my belt I would have been flying very blindly.
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I transitioned to this in 2018. It was called MLOps. I was a mobile developer before that. Transitioning was pretty easy at that time (it might be more competitive now). What I did: worked on an intensive ML project for myself and realized I enjoyed working across the ML stack. I wrote a blog post on the project here: https://www.nicksypteras.com/blog/aisu.html. Then I applied to an MLOps team and leveraged the project to demonstrate skills/experience. As for avoiding the math, you could probably get away with it but learning the basics will make everything way easier. I feel like without some basic ML math under my belt I would have been flying very blindly.