I can't use an ML runtime because I am using a Unity Catalog enabled cluster. This is a shared cluster capable of accessing the data I need. ML runitme on UC enabled clusters is not supported.

During training of the model, I get the following warning though: Maybe this is related?

warnings.warn(
2023/11/14 08:59:54 WARNING mlflow.models.model: Logging model metadata to the tracking server has failed, possibly due older server version. The model artifacts have been logged successfully under dbfs:/databricks/mlflow-tracking/f5f6e63a202844138b5fad3fddd0007a/e570c1e2b3ab4cb9b220046a5e5c3a64/artifacts. In addition to exporting model artifacts, MLflow clients 1.7.0 and above attempt to record model metadata to the tracking store. If logging to a mlflow server via REST, consider upgrading the server version to MLflow 1.7.0 or above. Set logging level to DEBUG via `logging.getLogger("mlflow").setLevel(logging.DEBUG)` to see the full traceback.
/databricks/python/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but IsolationForest was fitted with feature names