These are interesting points. I think it depends on where in the stack I'll be. At my last place I spent most of my time building and testing abstract ML functionality that I never deployed to production myself (other teams did that) and could be tested on a CPU in a reasonable amount of time. I can imagine the "other team" worked with the restrictions you mention. In my next role, I may well wear both hats.
Thanks. You differentiate ML from DL. Can you say what you mean by that in this context? Is working with DL a different experience than working with e.g. probabilistic modelling? Or do you mean e.g. tensorflow, pytorch, jax vs pandas, numpy, scikit-learn?
laprika0 OP t1_iup4bqt wrote
Reply to comment by lqstuart in [D] Machine learning prototyping on Apple silicon? by laprika0
These are interesting points. I think it depends on where in the stack I'll be. At my last place I spent most of my time building and testing abstract ML functionality that I never deployed to production myself (other teams did that) and could be tested on a CPU in a reasonable amount of time. I can imagine the "other team" worked with the restrictions you mention. In my next role, I may well wear both hats.