It reminds me an approach to data science and ML. It's way more common and essential for ML teams to log their attempts (experiments). There is a whole set of tools for this - experiments trackers. Primarily to even being able to compare and pick the best direction, but also to ensure reproducibility (in some areas it might be required).
ML/DS always seemed to me closer to science in its nature vs software engineering. Can be because of its nature as well - in a lot of cases it's a process of incremental improvements (vs - simplifying this a lot - let's say in SE we do a button that just works or not).
> ML/DS always seemed to me closer to science in its nature vs software engineering. Can be because of its nature as well - in a lot of cases it's a process of incremental improvements
Right, i have felt this way too (even though i am just a noob at ML/DS). For me it is the use of Statistics/Probability/Mathematics in driving understanding/intuition about the problem.
ML/DS always seemed to me closer to science in its nature vs software engineering. Can be because of its nature as well - in a lot of cases it's a process of incremental improvements (vs - simplifying this a lot - let's say in SE we do a button that just works or not).