OLAP vs OLTP will depend on your ML use case. Online predictions will likely be better served by an OLTP vs offline batch predictions being better served by OLAP.

OLAP use cases often involve a lot of extra complexity out of the gate, and something we're targeting is to help startups maintain the simplest possible tech stack early on while they are still growing and exploring PMF. At a high enough level, it should just work with any database that supports Postgres extensions, since it's all just tables going into algos, but the devil in big data is always in evaluating the performance tradeoffs for the different workloads. Maybe we'll eventually need an "enterprise" edition.

How about the difference between this and the Madlib project? Better ergonomics?

I've used Madlib in the past and although it was 'successful', the constraint was unfamiliarity with the library from our data scientists, who preferred the classic Python libraries.