diff --git a/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.py b/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.py
index ac7a48d..3f4df2d 100644
--- a/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.py
+++ b/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.py
@@ -26,8 +26,7 @@ def automl_eval_metrics(
   thresholds: str = '{"mean_absolute_error": 460}',
   confidence_threshold: float = 0.5  # for classification
 
-# ) -> NamedTuple('Outputs', [('deploy', str)]):  # this gives the same result
-) -> NamedTuple('Outputs', [('deploy', 'String')]):
+) -> NamedTuple('Outputs', [('deploy', str)]):
   import subprocess
   import sys
   subprocess.run([sys.executable, '-m', 'pip', 'install', 'googleapis-common-protos==1.6.0',
@@ -141,7 +140,7 @@ def classif_threshold_check(eval_info):
         with open(mlpipeline_metrics_path, 'w') as mlpipeline_metrics_file:
           mlpipeline_metrics_file.write(json.dumps(metrics))
         logging.info('deploy flag: {}'.format(res))
-        return res
+        return (res,)
 
       if classif and thresholds_dict:
         res, eresults = classif_threshold_check(eval_info)
@@ -159,14 +158,14 @@ def classif_threshold_check(eval_info):
         with open(mlpipeline_ui_metadata_path, 'w') as mlpipeline_ui_metadata_file:
           mlpipeline_ui_metadata_file.write(json.dumps(metadata))
         logging.info('deploy flag: {}'.format(res))
-        return res
-      return 'deploy'
+        return (res,)
+      return ('deploy',)
     except Exception as e:
       logging.warning(e)
       # If can't reconstruct the eval, or don't have thresholds defined,
       # return True as a signal to deploy.
       # TODO: is this the right default?
-      return 'deploy'
+      return ('deploy',)
 
 
 if __name__ == '__main__':
diff --git a/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.yaml b/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.yaml
index 757d5ee..80dbf48 100644
--- a/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.yaml
+++ b/ml/automl/tables/kfp_e2e/create_model_for_tables/tables_eval_metrics_component.yaml
@@ -38,7 +38,6 @@ implementation:
         thresholds  = '{"mean_absolute_error": 460}',
         confidence_threshold  = 0.5  # for classification
 
-      # ) -> NamedTuple('Outputs', [('deploy', str)]):
       )    :
         import subprocess
         import sys
@@ -153,7 +152,7 @@ implementation:
               with open(mlpipeline_metrics_path, 'w') as mlpipeline_metrics_file:
                 mlpipeline_metrics_file.write(json.dumps(metrics))
               logging.info('deploy flag: {}'.format(res))
-              return res
+              return (res,)
 
             if classif and thresholds_dict:
               res, eresults = classif_threshold_check(eval_info)
@@ -171,14 +170,14 @@ implementation:
               with open(mlpipeline_ui_metadata_path, 'w') as mlpipeline_ui_metadata_file:
                 mlpipeline_ui_metadata_file.write(json.dumps(metadata))
               logging.info('deploy flag: {}'.format(res))
-              return res
-            return 'deploy'
+              return (res,)
+            return ('deploy',)
           except Exception as e:
             logging.warning(e)
             # If can't reconstruct the eval, or don't have thresholds defined,
             # return True as a signal to deploy.
             # TODO: is this the right default?
-            return 'deploy'
+            return ('deploy',)
 
       def _serialize_str(str_value: str) -> str:
           if not isinstance(str_value, str):
diff --git a/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py b/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py
index b5938bd..5ac91d9 100644
--- a/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py
+++ b/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py
@@ -130,7 +130,7 @@ def automl_tables(  #pylint: disable=unused-argument
     eval_data=eval_model.outputs['eval_data'],
     )
 
-  with dsl.Condition(eval_metrics.outputs['deploy'] == 'd'):
+  with dsl.Condition(eval_metrics.outputs['deploy'] == 'deploy'):
     deploy_model = deploy_model_op(
       gcp_project_id=gcp_project_id,
       gcp_region=gcp_region,
diff --git a/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py.tar.gz b/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py.tar.gz
index 7a65e79..a125f40 100644
Binary files a/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py.tar.gz and b/ml/automl/tables/kfp_e2e/tables_pipeline_caip.py.tar.gz differ
diff --git a/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py b/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py
index b93f028..3185091 100644
--- a/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py
+++ b/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py
@@ -130,7 +130,7 @@ def automl_tables(  #pylint: disable=unused-argument
     eval_data=eval_model.outputs['eval_data'],
     ).apply(gcp.use_gcp_secret('user-gcp-sa'))
 
-  with dsl.Condition(eval_metrics.outputs['deploy'] == 'd'):
+  with dsl.Condition(eval_metrics.outputs['deploy'] == 'deploy'):
     deploy_model = deploy_model_op(
       gcp_project_id=gcp_project_id,
       gcp_region=gcp_region,
diff --git a/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py.tar.gz b/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py.tar.gz
index 2676987..cde8e1a 100644
Binary files a/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py.tar.gz and b/ml/automl/tables/kfp_e2e/tables_pipeline_kf.py.tar.gz differ