
Why businesses fail at machine learning - inlineint
https://hackernoon.com/why-businesses-fail-at-machine-learning-fbff41c4d5db
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andyidsinga
This article really resonates especially cooking innovation vs cooking
appliances innovation analogy .

I've been working with a buddy on some analytics service products for
biotech/life sciences (one is called Yukon Data Solutions). What we'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.

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's business?

Whats seems to be background / 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.

edit: I found Eugene Dubossarsky's thoughts on Decision Support interesting in
this podcast : [https://anchor.fm/datafuturology/episodes/1-Dr-Eugene-
Duboss...](https://anchor.fm/datafuturology/episodes/1-Dr-Eugene-Dubossarsky
---Chief-Data-Scientist--Principal-Trainer-e1fedo)

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mooreds
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't run server
infrastructure, the vast majority of companies shouldn'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.

