Not a data scientist myself, but plenty of data scientists in a consultancy company that I used to work in said that they have to implement variants of a limited set of models over and over again, because they couldn't reuse code and infrastructure. The project contracts demanded that all IP created by the consultant is the property of the client. This even caused some of the data scientists to lose motivation, because the job wasn't challenging to them intellectually as it involved setting up the same stuff again and again. Very rarely would their actual expertise be needed in the job.

I am not sure if this particular service solves the problem for them in any way, but to my ear it sounds like there is a need for code and infrastructure reuse in the data scientists domain that is ripe for innovation.

I'm pretty sure people said the exact same thing about Algolia when it was getting started (you have to tune search for your use case! How could you possibly use a search provider?!?)

Truth about the situation: - Transformers generalize well and don't need much fine tuning - OpenAI can probably fine tune for your use case better than you can - Getting new models into production takes 6 months to a year at companies of this size, if you did have Data Scientists in house, it might just be better to go with a solution like this for velocity - Not every company has the talent to make an in house ML program successful.

Except the point of these larger transformer models is they generalize well over a wide range of domains or only require a small amount of transfer learning for really specific domains.

I'd say they're perfect candidates for the API as a service model.