Co-author here. This post came out of a discussion with Adam, where we both realized that the advice we were giving to ML teams and ML Engineers to guide them to better results were very often process centric rather than model centric.

Many resources exist online about how to get a model to converge, and that’s not usually what makes or break a project.

Data acquisition, augmentation, model selection, and iterative exploration however seem quite rarely discussed compared to how important we have seen them be. This is our attempt at sharing this outside of our usual circles.

Platitudes on platitudes. "Be efficient." "Do it right."

Isn't that the goal of every company their Medium Blog :')

That doesn't mean it's bad.

Novel per se means nothing, for business the more the standards the better. In ML/DL for b2b we badly need unified best practices and, above all, sensitivity (ablation) protocols to demonstrate our models are neither overfitting nor cherrypicking.

I hate quotes but there's a single one I'll ever use because it's not only accurate but incredibly useful: "People need to be reminded more often than they need to be instructed."