I'm not sure what you mean by "creating ML black boxes", since I don't think most people working in ML are required to invent new types of ML approaches, in the

I'm not sure what you mean by "creating ML black boxes", since I don't think most people working in ML are required to invent new types of ML approaches, in the same way that Rails, Django, Sails works for most web apps. It's pretty straightforward to learn the ancillary data preparation, cross-validation, etc, and at the very least you can quickly apply a wide array of ML techniques (KNN, decision trees, LR, neural nets, etc.) with 3,4 LOC each (frameworks) and compare results, an approach you sometimes see on Kaggle. There's a reason "data science" bootcamps are putting out thousands of attendees on the job market - it's straightforward enough to get someone in the door for an interview after a few months of hard work (assuming they have either a strong programming or strong stats background).