SageMaker deployment: Improve description of `execution_role_arn_para…

…meter` (mlflow#859)

* Improve SageMaker execution role arn docs

* Address comment

mlflow/sagemaker/__init__.py

@@ -218,8 +218,17 @@ def deploy(app_name, model_path, execution_role_arn=None, bucket=None, run_id=No
     :param app_name: Name of the deployed application.
     :param path: Path to the model. Either local if no ``run_id`` or MLflow-relative if ``run_id``
                  is specified.
-    :param execution_role_arn: Amazon execution role with SageMaker rights.
-                               Defaults to the currently-assumed role.
+    :param execution_role_arn: The name of an IAM role granting the SageMaker service permissions to
+                               access the specified Docker image and S3 bucket containing MLflow
+                               model artifacts. If unspecified, the currently-assumed role will be
+                               used. This execution role is passed to the SageMaker service when
+                               creating a SageMaker model from the specified MLflow model. It is
+                               passed as the ``ExecutionRoleArn`` parameter of the `SageMaker
+                               CreateModel API call <https://docs.aws.amazon.com/sagemaker/latest/
+                               dg/API_CreateModel.html>`_. This role is *not* assumed for any other
+                               call. For more information about SageMaker execution roles for model
+                               creation, see
+                               https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html.
     :param bucket: S3 bucket where model artifacts will be stored. Defaults to a
                    SageMaker-compatible bucket name.
     :param run_id: MLflow run ID.