As someone with a toe in the deep learning research space, were you to look at commonly used ML code, then you'd find software engineering problems that are far

As someone with a toe in the deep learning research space, were you to look at commonly used ML code, then you'd find software engineering problems that are far bigger than just pickling. I think it underscores the distinction between computer science and software engineering; that is, the theoreticians of the former and those who actually deploy them in the latter. Researchers, especially sleep-deprived grad students, have borderline unreadable code for papers since they don't care about deployment. I'd imagine the enterprise engineers who create development pipelines, however, take such risks into consideration.