It all depends on the context. In academia it is a great tool. I can set up a couple of notebooks on our GPU server and give many students access to powerful GP

It all depends on the context. In academia it is a great tool. I can set up a couple of notebooks on our GPU server and give many students access to powerful GPUs without having to worry abbout shell access etc. Aditionally they are ready to go and do interesting things immediately and don't have to install the environment on their laptops (which might be win/linux/mac but at least these days that's easier but still extra work for them). I also use it a lot for experimenting, parameter tuning etc. It's not too bad to have it explicitly distinct from production level code. Run/tune/experiment in notebook, once you're happy with the model -> code it up in .py file(s). Also great for quick presentations :) However, the fast.ai team is actually doing a pretty solid job running everything off notebooks. So if I wanted to go that direction (and skip the .py files) it's that project I'd look at for how to do it.