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In particular treating datasets as "repos" analogous to how code can be versioned seems like a really good way forward.
I feel so validated by this article. I took two semesters of machine learning electives for my CS masters and feel nearly as ignorant and mystified as when I started. I worked so hard to create something useful and at the end of the day, my work felt like it was 96% example code with modifications hacked in to make it work. And in the end it was still terrible! At least now I know what people are talking about when d…
I think the real blocker is time; modern software devs are expected to be across the whole stack. You can't have one person write your backend, frontend, infra, db admin, build ml architecture, train model, etc. It's just too much.
It's just specialising really, wide and shallow vs narrow but deep domains. Forefront of ML stuff requires researchers specialised in that domain, same as any I guess.