Comment by monkeydust

About 6 years ago I invested in a ML as a startup company as an angel. The proposition was that they had a very nice workflow tool to take data, scrub it, partion it, build models against it, view performance and deploy it through and api. At the time there was nothing else out there really that was as polished and understandable to a business person. Then, very rapidly, the field got democratized. People were firing up notebooks, tensor flow popped into existence and their edge eroded. They were pitching to teams who had just hired data scientists who of course didn't want to buy something that made their job look easier! The other reality, which ties to this article, is that whilst they had to do a lot of hand holding with clients so the service cost became high although this wasn't custom model development but more training on the platform so they eventually worked out a way to package it in the sale as they got established. The company in the end changed course. They built out the product to focus on a single vertical that was getting traction in finance. This was huge contentious change and they lost some people along the way - but - it ultimately saved them from being wiped out. In the end the sold out to a much larger player in that vertical who was had no AI strategy and we all made a decent if not mind blowing return on our investment. Lots of lessons learnt on the way and it was a fun ride overall.