Welcome to the crappy ml world, where everything is clunky and barely works one time on one machine if you don’t touch anything on the computer, where software loses its meaning and saving Jupiter notebooks as py files is the norm, where the majority of “data scientist” means glorified labeling machines.
In their defense, stuff is moving forward at half the speed of light, so everybody has better things to do right now.
Also, investing serious time here on cleaner processes and better documentation of their specific implementation seems like a waste of time. I don't think much of any of this will still be in use next year, everybody will have moved on to more advanced projects.
But I agree, once things stabilize, the most popular models should invest in clean up a fair bit.
