Best way to manage train/test/val splits on AzureML
Registration is to make sharing and reuse easier so that you can retrieve the dataset by its name. If you do expect to reuse the test/validation sets in other experiments, then registering them make sense. However, if you are just trying to keep records of what you used for this particular experiment, you can always find those info via Run as you suggested.
I'm currently using AzureML with pretty complex workflows involving large datasets etc. and I'm wondering what is the best way to manage the splitting resulting of preprocessing steps. All my projects are built as pipelines fed by registered Datasets. I want to be able to track the splitting in order to easily retrieve, for example, test and validation sets for integration testing purposes.
What would be the best pattern to apply there ? Registering every intermediate set as different Dataset ? Directly retrieving the intermediate sets using the Run IDs ? ...
Thaanks