[Moto7451]

Which is good because a lot of such matching and ML use cases for products I’ve worked on at several companies fit into this. The problem I’ve seen is when decision making capabilities are inferred from/conflated with text classification and sentiment analysis.

In my current role this seems like a very interesting approach to keep up with pop culture references and internet speak that can change as quickly as it takes the small ML team I work with to train or re-train a model. The limit is not a tech limitation, it’s a person-hours and data labeling problem like this one.

Given I have some people on my team that like to explore this area I’m going to see if I can run a similar case study to this one to see if it’s actually a fit.

Edit: At the risk of being self deprecating and reductive: I’d say a lot of products I’ve worked on are profitable/meaningful versions of Silicon Valley’s Hot Dog/Not Hot Dog.