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I think what OP is saying is that any ostensible gains of a dedicated data scientist are lost later because they didn’t think about the solution through the len
by alexfromapex 6y ago
I think what OP is saying is that any ostensible gains of a dedicated data scientist are lost later because they didn’t think about the solution through the lens of the full tech stack. Maybe there was a much easier way to implement a model that could save a lot of time. Lots of scenarios where general knowledge leads to better decisions.
- mlthoughts2018 6y agoThat is premature abstraction 101 though. SRE folks will bring parochial philosophy and first principles constraints too early, not understanding the differences between ML system needs and other systems they’ve worked with. Assuming you could have correctly anticipated those design needs earlier, rather than going back to iterate only after you get concrete evidence of the specific changes you need, is a big trap and time waster. I don’t agree that you lose because the data scientist failed to consider production SRE patterns early enough. You lose when those SREs fail to take a YAGNI philosophy, let the unusual ML system’s chips fall where they may, and then intentionally go back and iterate on improving it.