Best Practices for Applying Deep Learning to Novel Applications
It's good general advice, but frankly I think it doesn't address some of the major pitfalls - namely the top one: personnel.In the very beginning it's stated:"In this report I assume you are (or have access to) a subject matter expert for your application."In my experience this is where it goes off the rails for most of the crowd that she is addressing. Not because they don't have someone, but because who they have isn't really a "subject matter expert."It's a muddy term anyway especially in the field of Machine Learning. Excluding for a moment the huksters and bold faced liars, within ML there is WIDE variance in competence, domain specificity and application specific capability within the field.The biggest capability gap that I have encountered when working with fantastic ML folks is that the ones that understand the mechanisms/algorithms/approaches best, are actually pretty terrible at delivering production code. That's not for lack of capability, it's simply because the bulk of their time has been spent in research - so they approach things very differently than application focused engineers. This is extremely relevant in this case because this is an application specific paper.There are a plethora of mine-fields in applications of ML, some of which are outlined here from a systems approach, but the majority of which are personnel issues in my experience, and "culture" issues - not to be confused with "culture fit" problems that exist elsewhere.