I have seen a few instances where companies start down the trained models/supervised learning path and then realize it won't work for their use cases. Next, the

I have seen a few instances where companies start down the trained models/supervised learning path and then realize it won't work for their use cases. Next, they switch to assisted learning/training using less human labeling, more heuristics, model ensembles (fast learners + slow learners), adversarial models, and so on. Finally they scrap the trained ML classifiers and use other numerical/data science methods. Basically they discover that training effective models takes too much time or too much data or both.