Try setting a remote artifact store (e.g., S3, GCS, WASBS). The issue happens because mlflow.log_model() saves artifacts locally before uploading. If artifact_uri is file:/..., MLflow tries to mkdir on client. Works fine only when tracking server and artifact store are on same machine. Fix by configuring remote artifact_uri.