Earlier quoted context omitted.
Transportation, Logistics, Supply Chain Management, (physical) Operations. I suspect the reason why deep learning has done so poorly in my domain is that the underlying data is a result of things that are very poorly abstracted as a "function". We have lots of discrete events, stateful buffering, hard non-linearities, discontinuities, numerical bounds, etc. It's more like learning business rules and physical process…
+1 For real world business problems that I most frequently encounter doing consulting, it's hard to beat Random Forests and/or Gradient Boosting. Truth be told, most business problems I encounter turn out to be largely helped by good old linear models.
Understandable models with clear intervention points are what most businesses seem to need once you get to digging around in their operations, customer and sales data.