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The AI companies the article is referencing are trying to provide those examples: smart compose, Netflix recommendation engines, etc. to customer companies with
by ec109685 7y ago
The AI companies the article is referencing are trying to provide those examples: smart compose, Netflix recommendation engines, etc. to customer companies without an army of PhD’s. However the article makes it clear that doing that in a general way is hard, much harder than a normal SaaS business.
- AndrewKemendo 7y agoIt's a difference without distinction. The services that ML based SaaS are offering are currently harder than the majority of other services. However you could have said the same about any number of services/products over the years that were in the early adopter curve, which I will state confidently that we're still in for ML. At the end of the day it's a hard SaaS just like VoIP and search and geoservices once were.
- ec109685 7y agoYeah, the article doesn't say don't do a ML SaaS. Just that it won't have 80% margins like a typical SaaS business and you have to specialize to give you any sort of scale or it's just a Service business.
- calebkaiser 7y agoI don't disagree that providing ML-features-as-a-service in a general way is much harder than a normal SaaS business. However, I don't necessarily believe those sorts of companies are the future of commercial ML. Rather, I believe practices like transfer learning and improved infrastructure (see the turnkey inference platforms mentioned previously) are enabling startups without armies of PhD's to build these ML-powered features themselves.