fix: correct airflow workflow for BYO estimators. (#1052)

The airflow workflow for BYO estimators has been broken since a fix
made on 09/13/2019. This resolves that issue.
Tests to ensure this does not happen again will be added as part of
the next airflow commit that prepares training jobs as part of
defining training configuration.

Author: knakad

src/sagemaker/estimator.py

@@ -561,7 +561,7 @@ def model_data(self):
             )["ModelArtifacts"]["S3ModelArtifacts"]
         else:
             logging.warning(
-                "No finished training job found associated with this estimator. Please make sure"
+                "No finished training job found associated with this estimator. Please make sure "
                 "this estimator is only used for building workflow config"
             )
             model_uri = os.path.join(
@@ -718,21 +718,20 @@ def transformer(
 
         if self.latest_training_job is None:
             logging.warning(
-                "No finished training job found associated with this estimator. Please make sure"
+                "No finished training job found associated with this estimator. Please make sure "
                 "this estimator is only used for building workflow config"
             )
             model_name = self._current_job_name
         else:
             model_name = self.latest_training_job.name
+            model = self.create_model(vpc_config_override=vpc_config_override)
 
-        model = self.create_model(vpc_config_override=vpc_config_override)
+            # not all create_model() implementations have the same kwargs
+            model.name = model_name
+            if role is not None:
+                model.role = role
 
-        # not all create_model() implementations have the same kwargs
-        model.name = model_name
-        if role is not None:
-            model.role = role
-
-        model._create_sagemaker_model(instance_type, tags=tags)
+            model._create_sagemaker_model(instance_type, tags=tags)
 
         return Transformer(
             model_name,
@@ -1716,7 +1715,7 @@ def transformer(
                 transform_env.update(env)
         else:
             logging.warning(
-                "No finished training job found associated with this estimator. Please make sure"
+                "No finished training job found associated with this estimator. Please make sure "
                 "this estimator is only used for building workflow config"
             )
             model_name = self._current_job_name