A broad covering term is MLOps (like DevOps). https://en.wikipedia.org/wiki/MLOps https://en.wikipedia.org/wiki/MLOps Armed with that term, we get (haven’t re

A broad covering term is MLOps (like DevOps). https://en.wikipedia.org/wiki/MLOps https://en.wikipedia.org/wiki/MLOps Armed with that term, we get (haven’t read): Machine Learning Operations (MLOps): Overview, Definition, and Architecture https://arxiv.org/abs/2205.02302 https://arxiv.org/abs/2205.02302 [+ps] Better resource: https://ml-ops.org/ https://ml-ops.org/ Based on my (limited) exposure to date, there is tremendous opportunity for software engineers and architects to make impact in ML systems. There is a pronounced lack of seasoned engineering talent (outside of big players like DataBricks, et al) and this knowledge gap sits behind an experience curve that mere IQ can’t jump over. Our experience as software architects and engineers is very valuable. Know this and recognize the value you will bring to the table.