
Where are the opportunities for machine learning startups? - ColinWright
http://www.vccafe.com/2015/12/01/where-are-the-opportunities-for-machine-learning-startups-guest-post/
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exelius
This article skirts around the issue, but the biggest (and most common)
mistake I've seen machine learning startups make is starting with an algorithm
and trying to productize it. When you do this, you end up with a solution
looking for a problem, which is never a recipe for a successful startup. So in
addition to finding a problem that fits your solution, you then also have to
build a product around it. And chances are, if your product is really great,
you can build it without fancy machine learning models and it would be just as
successful.

Machine learning, in my mind, is more of an enabler that makes great products
more 'magical' \- it's not really a product in itself. IMO it's a discipline
that lends itself well to a consulting model - hire some ML wizards, unleash
them along with a dev team on a problem for 6 months, then move on to the next
one. This is mostly the "infiltrating the professions" \- most consulting
companies organize themselves around market verticals for the exact same
reasons.

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ThomPete
Exactly!

Unless you sell machine learning algorithms of course :)

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exelius
That's basically consulting :) There are very few truly novel applications of
ML, and nearly all the major algorithms are open source. Using them together
correctly and interpreting the output is the hard part -- which is perfect for
scoping a consulting engagement.

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evandev
I work for a startup[0] that does quality control through machine learning for
the beverage industry. We're focused most on beer, followed by coffee and
spirits.

[0]: [https://gastrograph.com](https://gastrograph.com)

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andr3w321
Where do you find these ML wizard conusltants? I've been looking to hire
someone part time for a project to help build a model but most good people
have no time and I don't trust that anyone on elance or similar is any good.

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JasonCEC
The short answer is: _you don't_. That's not how machine learning works at
scale.

To quote from a great recent article: (I can't find the link, sorry)

It's one thing to create an excellent fraud detection model in R, and quite
another to build:

    
    
      - Fault-tolerant ingest of live data at scale that could represent fraudulent actions
      - Real-time computation of features based on the data stream
      - Serialization, versioning and management of a fraud detection model
      - Real-time prediction of fraud based on computed features at scale
      - Learning over all historical data
      - Incremental update of the production model in near-real-time
      - Monitoring, testing, productionization of all of the above
    
    

You don't build a data team out of a single person and tack on an easy model
to build a company - it takes a team to build a real data project, and those
teams are hard to find, hard to recruit, and hard to make successful.

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vdnkh
I've been getting my linkedin spammed by recruiters for a machine learning
startup. I ignore them all (despite actively looking for a job). Why? It's a
pie-in-the-sky idea which seems more interested in generating hype than
actually delivering results. To me, the product sounds too good to be true,
and is trying to replace a service which many people still do not trust humans
to do. The technology "is just no there yet" for a lot of the claims proposed
- and I doubt that a startup focused on making money can accomplish more than
academic institutions focused on research.

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tiredwired
A machine learning startup should use their machine to answer this question.

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dafrankenstein2
One idea is to use machine learning for training drones planes and
quadcopters....that will make a startup different than others! Actually
applying ML in a product will stand out in the market if the job is done
properly.

