The previous answer is applicable for managed MLflow as part of Databricks Machine Learning.
For Open Source MLflow please see the 4 different scenarios described in the Open Source MLflow website https://mlflow.org/docs/latest/tracking.html#how-runs-and-artifacts-are-recorded.
