Thanks for the answer! Registering some metadata as a model doesn’t feel correct to me. But anyway this is certainly not a show-stopper. Just wanted to clarify.

mostly the transformation of the pandas Dataframe - how the columns are added/removed/change types, NAs removed, rows removed etc

Make sense. BTW: you can manually add data visualization to a Dataset with dataset.get_logger().report_table(...)

Registering some metadata as a model doesn’t feel correct to me.

Yes I'm with you 🙂
BTW what kind of meta-data would need versions during the life time of a Task ?

Interesting!
Wouldn't Dataset (class) be a good solution ?

not sure - ideally I would like to see these tables (e.g. with series_name, series_dtype, number_of_non_na_values as columns) back to back in the GUI to track the transformations. I think it isn’t possible with Dataset . Anyway, this whole scenario is not a must have, but a nice to have.

Hi FiercePenguin76
Artifacts are as you mentioned, you can create as many as you like but at the end , there is no "versioning" on top , it can be easily used this way with name+counter.
Contrary to that, Models do offer to create multiple entries with the same name and version is implied by order. Wdyt?