The MLflow run was probably created either (a) via notebook autologging or (b) via a call to `mlflow.start_run()`. With (a), when the notebook first logs something to MLflow, it starts a run. But if the notebook is still active and attached to a cluster, and nothing has ended the run, then the run is still active ("UNFINISHED").

You can end the run with `mlflow.end_run()`.

Alternatively, you can wrap your model fitting code in a "with" statement:

with mlflow.start_run():
  # my ML code

This "with" syntax will automatically end the run once the ML code finishes.