[fundamental]

For code, git has won out among the other options. As per ML data+model versioning that area is still evolving and what the right choices are there depends on ML frameworks as well as your approaches to deploying new models.

Generally I'd view data and trained model versioning to be separate, but linked to the training code versioning. In an ideal world you end up with a system where data version+training code version is in the metadata of a given model version, but there's plenty of other aspects of the data science themed addons to consider.