Re
re-running this code produces the same printoutsI guess repeatable behaviour is a great default to have for, well, repeatability 🙂

I'm able to "randomize" my results by adding a seed pipeline argument and calling random.seed(seed)
within the pipeline and component. Results then change with change of seed.

I think most veteran ML practitioners are bitten at some point by randomising when they shouldn't and not randomising when they should. It would be nice to have some documentation proclaiming how randomness behaves when running tasks (in all their variations). E.g. Should I trust seeds to be reset or should I not assume anything and do my own control over seeds.