Agreed. I'm pretty sure it's due to all the different verticals coalescing, though I can't say for sure since some of the problems are consistent across different workflows as well.

You've got the data scientists tinkering away with the actual models where half of their stuff is in Jupyter notebooks. You've got the platform engineers sticking these models behind an API. DevOps making sure any model/code updates get propagated to prod in a safe and hassle free manner. Then you've got Infra who needs to make sure you actually have the specialized hardware to deploy these models onto.