@User Sorry for the delay in my response, was caught up in some urgent work deliverables.
Coming to your first point. For tracking purposes I'm using MLFlow because of the extent to which they provide autologging functionality. It's really easy to get up and running in no time. With kedro experiment tracking I would have to come up with a structure for saving the models, parameters and the other metadata too.
I've been using the dataset versioning since day one. The thing with MLFlow is that I can have multiple classification models in my registry. Every model has a specific lifecycle for which a team is responsible for running the test cases and pushing it into production. So from a management perspective, it becomes way easier to compare models, test the MVPs for specific algorithms and then push them into production accordingly. The other part is the ease of accessibility of fetching these models from the registry. I can use them directly for inference purposes.
I hope the response above answers your questions, if not then please let me know. I'd be happy to have a discussion regarding this