> So, you’re working on a new ML project. The first thing you do, model-wise – you implement a simple heuristic baseline and see where you stand. Second, you try a simple ML solution and analyze how much it improves the baseline. One thing you can try to do after this stage, at least what I like to do, is to try to estimate what would be your upper bound in terms of predictive performance, and let an AutoML solution squeeze the most out of your data and preprocessing.

100% agreed! And yet, most data scientists completely jump over the first two steps described here because it is not "sexy enough" and not "valuable"