Best Practices for Applying Deep Learning to Novel Applications
It&#x27;s good general advice, but frankly I think it doesn&#x27;t address some of the major pitfalls - namely the top one: personnel.<p>In the very beginning it&#x27;s stated:<p>&quot;In this report I assume you are (or have access to) a subject matter expert for your application.&quot;<p>In my experience this is where it goes off the rails for most of the crowd that she is addressing. Not because they don&#x27;t have someone, but because who they have isn&#x27;t really a &quot;subject matter expert.&quot;<p>It&#x27;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.<p>The biggest capability gap that I have encountered when working with fantastic ML folks is that the ones that understand the mechanisms&#x2F;algorithms&#x2F;approaches best, are actually pretty terrible at delivering production code. That&#x27;s not for lack of capability, it&#x27;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.<p>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 &quot;culture&quot; issues - not to be confused with &quot;culture fit&quot; problems that exist elsewhere.