5 ms·

Deep learning startup Skymind (YC W16) raises $3M, launches enterprise AI distro

85 points by vonnik 10y ago

paulsutter 10y ago
> After launching in 2014, Skymind now has half a dozen customers... Umm, is that a misprint?
- rgrieselhuber 10y ago
  I'm not familiar with the company but there is serious money in enterprise AI, speaking from experience. These could be very large customers.
  - agibsonccc 10y ago
    Hi, Yes the deal sizes are mid 6 figures or larger. We have a larger deal pipeline than that though. A lot more to come :). Red hat/oracle style on premise (non saas) business model. We usually target NON computer vison applications like fraud, preventative maintenance in data centers (predicting broken machines) and other mission critical applications. One example: http://insights.ubuntu.com/2016/04/25/making-deep-learning-accessible-on-openstack/ http://insights.ubuntu.com/2016/04/25/making-deep-learning-a... This kind of stuff is a swear word on hacker news but there's actually money in it. Fire away if you have specific questions though :).
    - harigov 10y ago
      What sort of value add do you provide on top of what the open source frameworks provide?
      - agibsonccc 10y ago
        Main thing is production deployment software and a service level agreement for models we build. In machine learning in production there are 2 phases: training and inference (usage) In training we have spark docker images where you can run cuda right from spark submit. In inference mode we sit on top of DC/OS by mesosphere embedding lightbend's (they created scala) micrsoservices technology conductr to scale out automatically on a mesos based cluster: http://www.slideshare.net/agibsonccc/deep-learning-in-production-with-the-best-66381424 http://www.slideshare.net/agibsonccc/deep-learning-in-produc... Here is more on our enterprise distribution SKIL: http://www.slideshare.net/agibsonccc/skil-dl4j-in-the-wild-meetup http://www.slideshare.net/agibsonccc/skil-dl4j-in-the-wild-m... If you're curious where the talent is, I cowrote the flagship oreilly book on deeplearning: http://shop.oreilly.com/product/0636920035343.do http://shop.oreilly.com/product/0636920035343.do We also employ deep learning phds doing everything from deep learning research in health care, ex nvidia, ex cloudera among others.
    - maxpupmax 10y ago
      What's your strategy to source relatively large deals like that? In general terms. Are you cold calling? conferences? Is it from online advertising? offline? referrals?
      - agibsonccc 10y ago
        Channels based sales and lead gen from conferences.
- Caligula 10y ago
  Probably not a misprint. They could be big enterprise customers.
  - agibsonccc 10y ago
    We don't work with startups :).
    - houstonishot 10y ago
      :)
    - Shanea93 10y ago
      I presume you mean "we don't currently work with startups". This is more of a financial issue than an ideological, enterprise only issue, right? I'm assuming if Grail (http://www.grailbio.com/ http://www.grailbio.com/) who launched in 2016 with $100,000,000 in funding were knocking at your door you would be more than happy to work with them?
      - agibsonccc 10y ago
        Right. Startups typically use python/ruby or commodity hadoop clusters on EMR. It's not an audience match and they don't have a budget anyways. For anyone else we have a very active open source community: https://gitter.im/deeplearning4j/deeplearning4j https://gitter.im/deeplearning4j/deeplearning4j
- doppenhe 10y ago
  Enterprise sales are hard.
  - agibsonccc 10y ago
    Indeed but that's what our team can do well. I've never been able to build a consumer product. Enterprise is just about understanding the incentives of the other party and knowing how to navigate the corporate latter.
- agibsonccc 10y ago
  FWIW we spent much of the time building the product and open source community. Many companies with infrastructure products like ours tend to "incubate" inside a big company first. We chose not to do that. So we spent much of our time just growing the user base first.
vonnik 10y ago
Hey folks - one of Skymind'a co-founders here. My co-founder Adam Gibson has been answering some questions I see. If you want to know anything about deep learning in production, please let us know and we'll share what we've seen.
- agibsonccc 10y ago
  I gave a presentation on this a bit ago: http://www.slideshare.net/agibsonccc/deep-learning-in-production-with-the-best-66381424 http://www.slideshare.net/agibsonccc/deep-learning-in-produc...
  - chopete 10y ago
    --- From the slide on list of Open Source tools and Algorithms • Kaggle(www.kaggle.com) is a good start for this - start with “somewhat real” problems • Use higher level tools - Keras(https://keras.io/ https://keras.io/), otherwise easy to get lost in weeds • Consider having a real world goal - eg: if you’re in real estate figure out how to use a simple CNN (not the latest algorithm) for image search • Depending on need consider integration with hadoop/Spark(http://spark.apache.org/ http://spark.apache.org/)
- dragon_king 10y ago
  What are some typical use cases that you have worked on with your clients, and can you give us an example of the impact it has had for a client?
  - vonnik 10y ago
    Typical use cases are: * fraud and anomaly detection * recommender systems * predictive analytics (churn, forecasting) * image recognition With image recognition, we hit 98% accuracy on a recent project. Until a few years ago, that was unheard of, and it's simply not possible with other algorithms, so for many companies, deep neural nets can make a significant difference. Here are two news stories about work we've done for clients: Making deep learning accessible on Openstack https://insights.ubuntu.com/2016/04/25/making-deep-learning-accessible-on-openstack/ https://insights.ubuntu.com/2016/04/25/making-deep-learning-... For Canonical, we built a solution that predicts server breakdowns. For France Telecom's mobile unit, Orange, we built a fraud detection solution using anomaly detection: http://www.orangesv.com/blog/orange-deep-learning-work-featured-in-wall-street-journal/ http://www.orangesv.com/blog/orange-deep-learning-work-featu...
kriro 10y ago
As someone who has worked at an open source company before I'd love to hear your thoughts on building a sustainable business around open source software. Most use cases I know are basically customization/consulting in enterprise settings build around a free base system. From reading about the company it seems that you want to monetize on ease of use with the distro? Do you have customers that specifically pick you because deeplearning4j is open or do you find that it's more of a nice to have or even don't care as long as it works type of situation? I'd also love to hear some reasoning for picking Apache 2.0 (i.e. a non-copyleft one). I've talked to at least one FLOSS founder who would have picked a different license in retrospect but feel like mostly the license matters less than most people think.

85 points by vonnik 10y ago

paulsutter 10y ago

> After launching in 2014, Skymind now has half a dozen customers... Umm, is that a misprint?

rgrieselhuber 10y ago
I'm not familiar with the company but there is serious money in enterprise AI, speaking from experience. These could be very large customers.
- agibsonccc 10y ago
  Hi, Yes the deal sizes are mid 6 figures or larger. We have a larger deal pipeline than that though. A lot more to come :). Red hat/oracle style on premise (non saas) business model. We usually target NON computer vison applications like fraud, preventative maintenance in data centers (predicting broken machines) and other mission critical applications. One example: http://insights.ubuntu.com/2016/04/25/making-deep-learning-accessible-on-openstack/ http://insights.ubuntu.com/2016/04/25/making-deep-learning-a... This kind of stuff is a swear word on hacker news but there's actually money in it. Fire away if you have specific questions though :).
  - harigov 10y ago
    What sort of value add do you provide on top of what the open source frameworks provide?
    - agibsonccc 10y ago
      Main thing is production deployment software and a service level agreement for models we build. In machine learning in production there are 2 phases: training and inference (usage) In training we have spark docker images where you can run cuda right from spark submit. In inference mode we sit on top of DC/OS by mesosphere embedding lightbend's (they created scala) micrsoservices technology conductr to scale out automatically on a mesos based cluster: http://www.slideshare.net/agibsonccc/deep-learning-in-production-with-the-best-66381424 http://www.slideshare.net/agibsonccc/deep-learning-in-produc... Here is more on our enterprise distribution SKIL: http://www.slideshare.net/agibsonccc/skil-dl4j-in-the-wild-meetup http://www.slideshare.net/agibsonccc/skil-dl4j-in-the-wild-m... If you're curious where the talent is, I cowrote the flagship oreilly book on deeplearning: http://shop.oreilly.com/product/0636920035343.do http://shop.oreilly.com/product/0636920035343.do We also employ deep learning phds doing everything from deep learning research in health care, ex nvidia, ex cloudera among others.
  - maxpupmax 10y ago
    What's your strategy to source relatively large deals like that? In general terms. Are you cold calling? conferences? Is it from online advertising? offline? referrals?
    - agibsonccc 10y ago
      Channels based sales and lead gen from conferences.
Caligula 10y ago
Probably not a misprint. They could be big enterprise customers.
- agibsonccc 10y ago
  We don't work with startups :).
  - houstonishot 10y ago
    :)
  - Shanea93 10y ago
    I presume you mean "we don't currently work with startups". This is more of a financial issue than an ideological, enterprise only issue, right? I'm assuming if Grail (http://www.grailbio.com/ http://www.grailbio.com/) who launched in 2016 with $100,000,000 in funding were knocking at your door you would be more than happy to work with them?
    - agibsonccc 10y ago
      Right. Startups typically use python/ruby or commodity hadoop clusters on EMR. It's not an audience match and they don't have a budget anyways. For anyone else we have a very active open source community: https://gitter.im/deeplearning4j/deeplearning4j https://gitter.im/deeplearning4j/deeplearning4j
doppenhe 10y ago
Enterprise sales are hard.
- agibsonccc 10y ago
  Indeed but that's what our team can do well. I've never been able to build a consumer product. Enterprise is just about understanding the incentives of the other party and knowing how to navigate the corporate latter.
agibsonccc 10y ago
FWIW we spent much of the time building the product and open source community. Many companies with infrastructure products like ours tend to "incubate" inside a big company first. We chose not to do that. So we spent much of our time just growing the user base first.

vonnik 10y ago

Hey folks - one of Skymind'a co-founders here. My co-founder Adam Gibson has been answering some questions I see. If you want to know anything about deep learning in production, please let us know and we'll share what we've seen.

agibsonccc 10y ago
I gave a presentation on this a bit ago: http://www.slideshare.net/agibsonccc/deep-learning-in-production-with-the-best-66381424 http://www.slideshare.net/agibsonccc/deep-learning-in-produc...
- chopete 10y ago
  --- From the slide on list of Open Source tools and Algorithms • Kaggle(www.kaggle.com) is a good start for this - start with “somewhat real” problems • Use higher level tools - Keras(https://keras.io/ https://keras.io/), otherwise easy to get lost in weeds • Consider having a real world goal - eg: if you’re in real estate figure out how to use a simple CNN (not the latest algorithm) for image search • Depending on need consider integration with hadoop/Spark(http://spark.apache.org/ http://spark.apache.org/)
dragon_king 10y ago
What are some typical use cases that you have worked on with your clients, and can you give us an example of the impact it has had for a client?
- vonnik 10y ago
  Typical use cases are: * fraud and anomaly detection * recommender systems * predictive analytics (churn, forecasting) * image recognition With image recognition, we hit 98% accuracy on a recent project. Until a few years ago, that was unheard of, and it's simply not possible with other algorithms, so for many companies, deep neural nets can make a significant difference. Here are two news stories about work we've done for clients: Making deep learning accessible on Openstack https://insights.ubuntu.com/2016/04/25/making-deep-learning-accessible-on-openstack/ https://insights.ubuntu.com/2016/04/25/making-deep-learning-... For Canonical, we built a solution that predicts server breakdowns. For France Telecom's mobile unit, Orange, we built a fraud detection solution using anomaly detection: http://www.orangesv.com/blog/orange-deep-learning-work-featured-in-wall-street-journal/ http://www.orangesv.com/blog/orange-deep-learning-work-featu...

kriro 10y ago

As someone who has worked at an open source company before I'd love to hear your thoughts on building a sustainable business around open source software. Most use cases I know are basically customization/consulting in enterprise settings build around a free base system. From reading about the company it seems that you want to monetize on ease of use with the distro? Do you have customers that specifically pick you because deeplearning4j is open or do you find that it's more of a nice to have or even don't care as long as it works type of situation? I'd also love to hear some reasoning for picking Apache 2.0 (i.e. a non-copyleft one). I've talked to at least one FLOSS founder who would have picked a different license in retrospect but feel like mostly the license matters less than most people think.