as someone in ML, I see myself wanting the opposite. ML researchers drown their algorithms in huge tables of results, effectively spending time on "how well" r

as someone in ML, I see myself wanting the opposite. ML researchers drown their algorithms in huge tables of results, effectively spending time on "how well" rather than the "what". It often leads to things being added as long as they are better, with the conclusion of it being a gargantuan monster of models and hand-engineered changes. All with no one understanding how the whole things works as a single unit. Flow charts are incredibly effective as the top most layer of abstraction. Does the whole process, when viewed in an end-2-end manner, make sense ? We dive into the details only if it passes that sniff test of a flow chart. I might be missing the point being made here, but they can claw flowcharts from my cold dead hands.