Previously my job as a DS was very easy and comfortable- just use a jupyter notebook, generate some boilerplate analysis, sometimes build some simple ML models. One time clicked around in Azure to deploy a model.
One month in as a MLE. The transition has been rough. At my previous job, I rarely used git, I had no sense of system design, I had zero clue about writing production code or unit tests. I never reviewed a PR or contributed to a large codebase. I didn't know anything about ops or DRI (oncall). I mean, I have a CS degree, but these things weren't taught in the classroom.
There is so much to learn and I like the product I work on, but sometimes I feel as if I bit off more than I could chew. Am working many hours just to keep my head above water. Many of my colleagues have PhDs, and they seem really solid on the engineering side as well. Feel I can really learn a lot from them.
The one thing which I can really appreciate now is that ML which is used in production on my team is much more complex than some one liner I was using in sklearn. There are entire research teams in US, India, and China dedicated to building huge state of the art models. Really cool, and at the same time daunting, to be working alongside these geniuses.
TC 210k.
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comments
What kinds of unit tests do you write for ML models?
Also, won’t score/feature distribution shift be post-launch?