[edejong]

Well, if you dive into data science and ML, you'll see it is much more than just a bit of linear algebra. Some topics (and as a data engineer, I am not an expert):

- Distributed computing

- Feature selection

- Budgeting / accounting of experimental design (these TPU clusters are not cheap)

- ML architecture

- Involvement of domain experts (multidisciplinary teams)

- Storage

The whole list is much longer, but just some topics to think about. For me the term "AI", although overly broad, come to mean the engineering and organisation needed to pull these kind of projects.