The behaviour I'd like to achieve is that any artefact is automatically saved to an S3 bucket, possibly without having the Data Scientist having to configure much on their side.

Right now, we are storing artefacts in the fileserver, and we have to make sure that we use output_uri=True in the Task.init call to have artefacts uploaded to ClearML fileserver.

What's the ideal setup to keep the boilerplate for DS code minimal?