
Blood, Sweat and Years: Raising Money as a Deep Learning Startup - vonnik
https://mattermark.com/blood-sweat-years-raising-money-deep-learning-startup/
======
agibsonccc
Hey folks the other skymind founder here. - 1 piece of inspiration I want to
throw out there.

Many open source startups pre built their product to commercialize before the
company. (red hat -> linux, hadoop -> cloudera) . We ended up doing both at
the same time (do not do this unless you want to tear your hair out).

When it started it was actually just the 2 of us with me writing all the code.
The thing that made it work: We put it out there and got user feedback and
paid very close attention to users.

A lot of machine learning startups have their "secret algorithm that's
actually just using an open source python based deep learning toolkit for
their mvp" .

For these product based deep learning startups, there isn't much actual deep
learning going on. Half of the appeal here is focusing on a specific domain
and accumulating data and expetise/partners in that domain.

We did the opposite by "giving it away".

This is ultimately what culminated in our support first culture as well as the
bulk of our engineering hiring.

Having our customers,users, and engineering team co located has been a
blessing in disguise.

Lastly: You should also write a book while doing a startup.
[http://shop.oreilly.com/product/0636920035343.do](http://shop.oreilly.com/product/0636920035343.do)

If there's any particular questions on any of these things happy to answer.

------
guiomie
"Products. Now is the time to build products that are infused invisibly with
AI." ... What is the best way to get started on this? I'd like to learn
practical ML/DeepLearning algorithms, not the implementation, but how to apply
known theory to problems and with a framework.

~~~
agibsonccc
Take something pretrained and slap a GUI around it. Get used to the idea of
running a model attached to a website.

From there, it's really just normal product knowledge. Eg: what will make
money?

Don't "embed machine learning" for fun - pick a simple problem with real value
like a normal customer analytics problem to start. Everyone has web traffic -
try to see if there's any value you can extract from that. It could be
optimizing conversions, churn prediction, or anything similar to that.

------
vonnik
I wrote this post and if anyone has any questions about the process of fund-
raising, I'll try to answer them. Got my pearls of wisdom in a little bag next
to the laptop. ;)

~~~
dxbydt
This - > a quick warning to machine-learning grad students: You’re not going
to raise on an algorithm...they are going to ask you: Why should the world
care?

And this - > To get users, you need a product, to get a product, you need
funding, and to get funding, you need users. I’m sure you can appreciate the
catch-22

But especially this- >bootstrapping marathon of many nights

Thanks for writing this. Its hard to empathize with these crazy folk, until
you become one of them & then you realize Woah! Its a relentless sleep-
deprived grind. Haven't felt so groggy since grad school. That said, light at
the end of tunnel etc. Keeps me going. I actually use DL4J on the backend, so
thanks again!

~~~
vonnik
Wow, it's always great to meet a DL4J user! Hope you've tapped into our
community on Gitter:
[https://gitter.im/deeplearning4j/deeplearning4j](https://gitter.im/deeplearning4j/deeplearning4j)
Let us know if we can help.

------
trendia
Recommendation: on your website [0] you mention a case study for detecting
financial fraud. It seems to talk about what _could_ be done rather than what
_has_ been done.

So, I think you may benefit by showing specific examples of catching fraud.
For instance: what is your performance compared to, say, human auditors? What
sort of features can you find that human auditors cannot? And what is your
false positive / true positive (ROC) like?

[0] [https://skymind.io/finance](https://skymind.io/finance)

~~~
vonnik
Hi there! A lot of the fraud detection we do is actually anomaly detection,
where we're not training a classifier on a labeled dataset, but instead
creating a model that can surface unusual behavior. The subset of data that we
surface is then passed to human analysts. What they care about, generally, is
a low false positive rate, because false positives waste their time. We talk
about this a bit more here: [https://skymind.io/case-
studies/orange](https://skymind.io/case-studies/orange) Sorry if that doesn't
quite answer your question.

~~~
yingxie3
For fraud detection such as what you did for orange, is there a rule of thumb
on how much data one would need? Before heading down the path I would like to
know whether my problem is solvable given the amount of data I have. Thanks in
advance!

~~~
agibsonccc
We were in the terabytes. A viable proof of concept could be built with a few
gigs of transactions to validate the idea though.

The main bit with orange that isn't in the marketing material: We used
unsupervised methods for this, not supervised.

------
Teodolfo
It is probably hard for companies like skymind with no recognized experts.
Getting a big name on board isn't always possible however. And investors are
right to be leery given the number of people branding themselves as deep
learning experts without any credible track record of results.

~~~
agibsonccc
To be fair, I'm not Andrew ng by any means, but I'm not "unknown":

[https://www.youtube.com/results?search_query=adam+gibson+dee...](https://www.youtube.com/results?search_query=adam+gibson+deep+learning)

[http://www.slideshare.net/agibsonccc](http://www.slideshare.net/agibsonccc)

[http://shop.oreilly.com/product/0636920035343.do](http://shop.oreilly.com/product/0636920035343.do)

I frequent the big data circles quite a bit. This is our main audience though,
not the DL research folks.

As far as my customers go that's actually enough. You're right it's still hard
though. I've done my fair share of outreach and speaking though. Anyone who
does their research will fine ample credit that we aren't just random folks
off the street.

We built up that credibility over time though. I'm still the creator of the
dl4j framework itself. So in practice people see we can build software.

~~~
chronic6l
I'm a medium-name in deep learning (10K unique visitors to my personal website
each day) and I have never heard of any of the Skymind founders. Sorry but
this all seems like a PR stunt for Skymind since nearly everyone is using
Python/C++ for deep learning and not Java/Scala.

I've heard of Skymind, just not the founders.

~~~
vonnik
I'm not sure what "it seems like a PR stunt" means in this context. It's a
post I wrote for a blog, so in that sense, it _is_ a PR stunt. But the post
reflects our experience, so in that sense, it's not an exaggeration. Python
people think that nearly everyone is using Python/C++ for deep learning.
That's because most of them are researchers, so they are correct from their
perspective, but it has limits. There's a lot of DL happening on the JVM in
large organizations. That's who we serve.

------
bluetwo
I think the point about applying the AI to another field is really important.
I think this is the key to how AI is going to generate value, and it kind of
seems overlooked. I hope to see some more brainstorming done it this area that
gets us beyond facial recognition and recommendation engines.

------
webmaven
What opportunities do you think exist for building new platforms one level
higher than the established Machine Learning as a Service platforms with their
network effects?

By way of analogy, Cloud Foundry is a PaaS that can run on top of any of the
established IaaS platforms.

~~~
agibsonccc
Hi, Chris' cofounder here. The problem with this would be defining "apps". You
would be limited by the features each one offered. While it is possible, half
of the appeal of machine learning is being able to define your own inputs.

The other thing here: It would more or less be redundant. If one is more
accurate than the other you're not going to bother using them really.

Maintaining something like that would be a nightmare (compatibility issues an
updates among others). The ROI here doesn't seem to be there for me.

I might be the wrong person to answer this though: My prejudice against SAAS
Infra (eg: ML as service) runs pretty deep. I tried building one before (NLP
focused) back in 2012 and learned developers don't like to pay and they will
always ask for more features. I'm also not the right kind of founder to build
a SAAS business though. I don't like the idea of chasing after 10s of millions
of people for $5/month when I could produce value for 1 company I know and
sell software several times and make equivalent revenue (hence skymind's on
premise focus)

~~~
webmaven
Thanks for sharing your perspective.

Note: Whether the thing you build on top is a product or a service is open.
Cloud Foundry as an example is really more of a PaaS _product_ (which can be
deployed on prem or on various IaaS platforms).

As for having a few big customers vs. many small ones, there are advantages to
both approaches (enterprise sales vs. self-serve, higher margins vs. lower
volatility, etc.).

~~~
agibsonccc
As always. I am better at enterprise than high volume self serve .

We do our stuff we build on top closed source. Easier to monetize. This is
known as open core.

