The article doesn't convince me. It can be summarized as "don't overengineer" but quite frankly these days ML/DL is so easy to apply from a technical point of

The article doesn't convince me. It can be summarized as "don't overengineer" but quite frankly these days ML/DL is so easy to apply from a technical point of view (taking care of the data or fully grasping the things you apply is another issue) that I don't see why one wouldn't at least try to use it. I don't see why a ML-algorithm couldn't grab the first name for example. I mean if your argument is "just use SQL" my counterargument is "I agree but I can just try ML as SQL on steroids". If you already have well curated data that you run the SQL on you might as well play around with it in an ML setting. "Customer with largest basket" might work fine but why not try to prod the data to check for other interesting things. Same for the POD example. Why not at least try to see if a combination of variables might yield more interesting results than the simple stuff that might work. Occams razor should not cut out all curiosity :D I like the overall idea of "try the simple stuff first" but quite frankly these days you can run very good ML with pretty much all it takes to do SQL queries (assuming you train your models on a separate machine).