Why businesses fail at machine learning

This article really resonates especially cooking innovation vs cooking appliances innovation analogy .<p>I&#x27;ve been working with a buddy on some analytics service products for biotech&#x2F;life sciences (one is called Yukon Data Solutions). What we&#x27;ve found looking at other products in the space is they often require the customer to focus a lot on processes, tooling, infrastructure - and so some degree algo research - in order to use the product.<p>What is key, we believe, is a focus on the analytics recipes and available algo ingredients in order to support decisions: how are the analytics recipes and resulting reports going to help decide on the next steps for the customer&#x27;s business?<p>Whats seems to be background &#x2F; secondary? explaining the tools, libraries, languages, cloud infrastructure, automation, data lakes etc that a service may or may not use behind the scenes to achieve the goal. Are these needed - yes at different levels and different times depending on the customer - but focus on the recipe, ingredients and decision support seems key.<p>edit: I found Eugene Dubossarsky&#x27;s thoughts on Decision Support interesting in this podcast : <a href="https:&#x2F;&#x2F;anchor.fm&#x2F;datafuturology&#x2F;episodes&#x2F;1-Dr-Eugene-Dubossarsky---Chief-Data-Scientist--Principal-Trainer-e1fedo" rel="nofollow">https:&#x2F;&#x2F;anchor.fm&#x2F;datafuturology&#x2F;episodes&#x2F;1-Dr-Eugene-Duboss...</a>

Great post from a bigwig at Google about the difference between building the infrastructure of machine learning and applying that infrastructure. I think that just like the vast majority of companies shouldn&#x27;t run server infrastructure, the vast majority of companies shouldn&#x27;t be involved in building the nuts and bolts of ML systems. Instead, use one of the big cloud providers or open source frameworks until your needs exceed the offering.