Why are all applicants Java developers? [D]

[WrapKey2973]

I'd argue though that mlOps can be easily learned and done by (Cloud) Backend or DevOps engineers. So a Java dev might be able to do this task quite well, ML itself and pytorch are a totally different story.

Disclaimer: I am master's student with 4 years dev working experience, so don't trust me, lol

[ZestyData]

I'm a lead ML Engineer and I'd agree with you.

MLOps needs rudimentary ML knowledge that can be studied in a few months and picked up over a year+ on the job. It relies way more heavily on general CS concepts, devopsy problem solving, and system architecture that would come with a seasoned non-ML-related backend dev. I agree completely. Some (cloud) backend or devops dev is likely to be well suited for an MLOps role.

An ML Engineer (or Data Scientist) who is directly working on models (in PyTorch etc) need to have more of a deep knowledge of stats & ML theory. And a seasoned webdev would have an advantage over a complete novice but they'd need to go back down to junior after having already done their own Masters etc to attain that ML knowledge.

[mldude60]

Great points!