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I agree that this is a common misconception people have about ML and how it fits into our world. If you want to see successful ML products, you don't have to fi

I agree that this is a common misconception people have about ML and how it fits into our world. If you want to see successful ML products, you don't have to find some AGI stealth mode startup—just look at your phone: - Gmail's Smart Compose - Netflix's Recommendation Engine - Uber's ETA Prediction ML functionality is becoming a standard feature in software, and in my opinion, that's the real "ML Revolution." As you mention, turnkey inference tools like Cortex (full disclosure/shameless plug: I'm a contributor) are making this accessible to virtually all eng teams.

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 ago
  It'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 ago
    Yeah, 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 ago
  I 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.

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 ago
It'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 ago
  Yeah, 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 ago
I 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.