[naomisperfume]

This is a valid point, but I think in part the data science / machine learning cycle doesn't reward careful development like software engineering does.

Most of the time you're just testing the viability of things and just want to fail fast if they don't work. Getting too attached to some pipeline or abstractions is usually a bad idea.

I don't think this excuses bad code, but I kinda get it when it happens, since you can't just use best practices from the beginning like in software development