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You can find the MLflow version in the runtime release notes, along with a list of every other library provided. E.g., for DBR 8.3 ML, you can look at the release notes for AWS, Azure, or GCP.The MLflow client API (i.e., the API provided by installi...

You can use explain to see what type of physical and logical plans are getting created . This is the best way to see difference , but as mentioned in the question , it should give the same physical plan

Our documentation provides nice examples of preparing image data for training and inference.Training: See docs for AWS, Azure, GCPInference: See reference solution for AWS, Azure, GCP

Less use of Interactive cluster and more use of job cluster can one of the way above others

Follow the instruction at Share models across workspaces.

Online store is for real time inferencing, in most case you will use the offline store.

The following resources provide more detail on this:Databricks model registry example notebook: https://docs.databricks.com/_static/notebooks/mlflow/mlflow-model-registry-example.htmlDatabricks model lifecycle - https://docs.databricks.com/applicatio...

https://databricks.com/blog/2017/05/18/taking-apache-sparks-structured-structured-streaming-to-production.html

There is a 500 character limit to strings in columns which is non-configurable. To see the full contents of the column, you can either use the tooltip to expand the cell or download the full results.