Hi Skip -
We have an article on how to deploy a DataRobot scoring code jar model on Databricks, and monitor it from DataRobot MLOps. This leverages an agent architecture that reports performance and feature/prediction data back to DataRobot. You can find it here.
You can additionally monitor non-DataRobot models as well. Note the actual code calls simply send performance stats (number of rows, time to score) and data (features, prediction/model outputs) back to DataRobot, which has an entry created for the deployment based on model characteristics and a training dataset you provide. The model itself is independent from the monitoring operations; any custom model, or model created by another platform, could be monitored.