Conjecture: production quality of ml code has mostly to do with how heuristics are designed and battle tested and almost nothing to do with how the training/inference pipeline is constructed.
Just because the challenge is relatively trivial to solve, doesn't make it any less important though. Experiment management, and the transition to production, is recognised as having potentially high impact to successful delivery. My understanding is that this takes care of details, which can otherwise get forgotten in the race for the best model. But YMMV.