Reminds me of John Foreman (data scientist at Mailchimp) and his excellent blog, including this piece, Surviving Data Science at the Speed of Hype: <a href="http:&#x2F;&#x2F;www.john-foreman.com&#x2F;blog&#x2F;surviving-data-science-at-the-speed-of-hype" rel="nofollow">http:&#x2F;&#x2F;www.john-foreman.com&#x2F;blog&#x2F;surviving-data-science-at-t...</a><p>&gt; <i>Over the course of my career, I&#x27;ve built optimization models for a number of businesses, some large, like Royal Caribbean or Coke, some smaller, like MailChimp circa 2012. And the one thing I&#x27;ve learned about optimization models, for example, is that as soon as you&#x27;ve &quot;finished&quot; coding and deploying your model the business changes right under your nose, rendering your model fundamentally useless. And you have to change the optimization model to address the new process...</i><p>&gt; <i>Process stability and &quot;speed of BLAH&quot; are not awesome bedfellows. Supervised AI models hate pivoting. When a business is changing a lot, that means processes get monkeyed with. Maybe customer support starts working in different shifts, maybe a new product gets released or prices are changed and that shifts demand from historical levels, or maybe your customer base changes to a younger demographic than your ML models have training data for targeting...</i><p>Until the machines are literally thinking for themselves, it seems that being able to engineer a stable infrastructure for rapid debugging and sampling of feedback is as paramount as being able to do the math for the modeling.