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MLOps is about enabling reproducibility of previous outputs; it has nothing to do with reinforcing any pattern. It's literally "DevOps for Data Science". It's a

• usgroup 4y ago
  > MLOps is about enabling reproducibility of previous outputs; it has nothing to do with reinforcing any pattern. This sentence seems to contradict itself. Otherwise, I'm familiar with MLOps :-) Wikipedia describes it as the intersection of ML, devops and data engineering, which is also how I've experienced it. What data is available (already "plugged in") paints a path of least resistance for a data scientist (DS) who then goes on to look for solutions within that data (see joke above) -- not necessarily, but it tends to go like that. The DS cuts a black box which MLOps tunes, monitors and re-trains: more path of least resistance. This ends up reflecting in hiring decisions, and so on. Again, not necessarily the case, just a dominant pattern I see. I suspect part of the reason is the movement of devs into MLEng and MLOps and analysts/devs into DS because analysts/devs typically do not have research backgrounds. It causes DS -- which initially was almost exclusively PhDs -- to be less about describing phenomena using maths and more about fitting black boxes. Further, "ML" as taught in Google courses (centered on Tensorflow) targets devs and literally paints the process of modelling as features, topologies and parameters. There is a selection pressure in that direction -- presumably due to labour supply, IMO to the detriment of the craft, but hey-ho.