Another general question: it's clear that the design of Kedro is quite heavily inspired around ML use cases. Is it still advised to use Kedro for data science projects that have no ML involved?

I would like to think so - Kedro gives you a generic template, a mechanism for defining transformation logic, a system for managing configuration and encourages you to build testable, modular code

Thanks for the advice!