Thank you MuddyCrab47 !
Regrading model versioning:
All models are logged automatically by trains (no need so specify it, as long as you are using one of the automagically connected frameworks: PyTorch/keras/TF/SKlearn)
You can see see how it looks like on the demoapp:
https://demoapp.trains.allegro.ai/projects/5371015f43f043b1b4ad7203c1ff4a95/models

Regrading Dataset management, we have a simple workflow demonstrated below, bascially using artifacts as dataset storage, with very easy interface for retrieving them (including cache),
The actual Dataset ID is the experiment uploaded/created it.
See here:
https://github.com/allegroai/events/blob/master/odsc20-east/generic/dataset_artifact.py
https://github.com/allegroai/events/blob/master/odsc20-east/generic/process_dataset.py
https://github.com/allegroai/events/blob/master/odsc20-east/scikit-learn/sklearn_jupyter.ipynb

A more robust dataset management is available on the enterprise edition (including searchability, debiasing etc.)