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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
by agibsonccc 10y ago
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 10y agoThanks 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 10y agoAs 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.