diff --git a/google/cloud/automl_v1beta1/services/tables/tables_client.py b/google/cloud/automl_v1beta1/services/tables/tables_client.py
index ed96e98b..566f509c 100644
--- a/google/cloud/automl_v1beta1/services/tables/tables_client.py
+++ b/google/cloud/automl_v1beta1/services/tables/tables_client.py
@@ -16,8 +16,9 @@
 
 """A tables helper for the google.cloud.automl_v1beta1 AutoML API"""
 
-import pkg_resources
+import copy
 import logging
+import pkg_resources
 import six
 
 from google.api_core.gapic_v1 import client_info
@@ -107,7 +108,7 @@ def __init__(
         client=None,
         prediction_client=None,
         gcs_client=None,
-        **kwargs
+        **kwargs,
     ):
         """Constructor.
 
@@ -258,7 +259,6 @@ def __dataset_from_args(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
     ):
         if dataset is None and dataset_display_name is None and dataset_name is None:
             raise ValueError(
@@ -275,7 +275,6 @@ def __dataset_from_args(
             dataset_name=dataset_name,
             project=project,
             region=region,
-            **kwargs
         )
 
     def __model_from_args(
@@ -285,7 +284,6 @@ def __model_from_args(
         model_name=None,
         project=None,
         region=None,
-        **kwargs
     ):
         if model is None and model_display_name is None and model_name is None:
             raise ValueError(
@@ -301,7 +299,6 @@ def __model_from_args(
             model_name=model_name,
             project=project,
             region=region,
-            **kwargs
         )
 
     def __dataset_name_from_args(
@@ -311,7 +308,6 @@ def __dataset_name_from_args(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
     ):
         if dataset is None and dataset_display_name is None and dataset_name is None:
             raise ValueError(
@@ -325,15 +321,12 @@ def __dataset_name_from_args(
                     dataset_display_name=dataset_display_name,
                     project=project,
                     region=region,
-                    **kwargs
                 )
 
             dataset_name = dataset.name
         else:
             # we do this to force a NotFound error when needed
-            self.get_dataset(
-                dataset_name=dataset_name, project=project, region=region, **kwargs
-            )
+            self.get_dataset(dataset_name=dataset_name, project=project, region=region)
         return dataset_name
 
     def __table_spec_name_from_args(
@@ -344,7 +337,6 @@ def __table_spec_name_from_args(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
     ):
         dataset_name = self.__dataset_name_from_args(
             dataset=dataset,
@@ -352,12 +344,9 @@ def __table_spec_name_from_args(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
-        table_specs = [
-            t for t in self.list_table_specs(dataset_name=dataset_name, **kwargs)
-        ]
+        table_specs = [t for t in self.list_table_specs(dataset_name=dataset_name)]
 
         table_spec_full_id = table_specs[table_spec_index].name
         return table_spec_full_id
@@ -369,7 +358,6 @@ def __model_name_from_args(
         model_name=None,
         project=None,
         region=None,
-        **kwargs
     ):
         if model is None and model_display_name is None and model_name is None:
             raise ValueError(
@@ -382,14 +370,11 @@ def __model_name_from_args(
                     model_display_name=model_display_name,
                     project=project,
                     region=region,
-                    **kwargs
                 )
             model_name = model.name
         else:
             # we do this to force a NotFound error when needed
-            self.get_model(
-                model_name=model_name, project=project, region=region, **kwargs
-            )
+            self.get_model(model_name=model_name, project=project, region=region)
         return model_name
 
     def __log_operation_info(self, message, op):
@@ -426,7 +411,7 @@ def __column_spec_name_from_args(
         column_spec_display_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         column_specs = self.list_column_specs(
             dataset=dataset,
@@ -436,7 +421,7 @@ def __column_spec_name_from_args(
             table_spec_index=table_spec_index,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
         if column_spec_display_name is not None:
             column_specs = {s.display_name: s for s in column_specs}
@@ -481,6 +466,29 @@ def __ensure_gcs_client_is_initialized(self, credentials, project):
                 project=project, credentials=credentials
             )
 
+    def __process_request_kwargs(self, request, **kwargs):
+        """Add request kwargs to the request and return remaining kwargs.
+
+        Some kwargs are for the request object and others are for
+        the method itself (retry, metdata).
+        
+        Args:
+            request (proto.Message) The request object.
+        
+        Returns:
+            dict: kwargs to be added to the method.
+        """
+
+        method_kwargs = copy.deepcopy(kwargs)
+        for key, value in kwargs.items():
+            try:
+                setattr(request, key, value)
+                method_kwargs.pop(key)
+            except AttributeError:
+                continue
+
+        return method_kwargs
+
     def list_datasets(self, project=None, region=None, **kwargs):
         """List all datasets in a particular project and region.
 
@@ -523,17 +531,22 @@ def list_datasets(self, project=None, region=None, **kwargs):
                 to a retryable error and retry attempts failed.
             ValueError: If required parameters are missing.
         """
-        return self.auto_ml_client.list_datasets(
-            parent=self.__location_path(project=project, region=region), **kwargs
+
+        request = google.cloud.automl_v1beta1.ListDatasetsRequest(
+            parent=self.__location_path(project=project, region=region),
         )
 
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+
+        return self.auto_ml_client.list_datasets(request=request, **method_kwargs)
+
     def get_dataset(
         self,
         project=None,
         region=None,
         dataset_name=None,
         dataset_display_name=None,
-        **kwargs
+        **kwargs,
     ):
         """Gets a single dataset in a particular project and region.
 
@@ -586,7 +599,10 @@ def get_dataset(
             )
 
         if dataset_name is not None:
-            return self.auto_ml_client.get_dataset(name=dataset_name, **kwargs)
+            request = google.cloud.automl_v1beta1.GetDatasetRequest(name=dataset_name,)
+            method_kwargs = self.__process_request_kwargs(request, **kwargs)
+
+            return self.auto_ml_client.get_dataset(request=request, **method_kwargs)
 
         return self.__lookup_by_display_name(
             "dataset",
@@ -633,14 +649,16 @@ def create_dataset(
                 to a retryable error and retry attempts failed.
             ValueError: If required parameters are missing.
         """
-        return self.auto_ml_client.create_dataset(
+        request = google.cloud.automl_v1beta1.CreateDatasetRequest(
             parent=self.__location_path(project, region),
             dataset={
                 "display_name": dataset_display_name,
                 "tables_dataset_metadata": metadata,
             },
-            **kwargs
         )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+
+        return self.auto_ml_client.create_dataset(request=request, **method_kwargs)
 
     def delete_dataset(
         self,
@@ -649,7 +667,7 @@ def delete_dataset(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Deletes a dataset. This does not delete any models trained on
         this dataset.
@@ -709,13 +727,15 @@ def delete_dataset(
                 dataset_display_name=dataset_display_name,
                 project=project,
                 region=region,
-                **kwargs
+                **kwargs,
             )
         # delete is idempotent
         except exceptions.NotFound:
             return None
 
-        op = self.auto_ml_client.delete_dataset(name=dataset_name, **kwargs)
+        request = google.cloud.automl_v1beta1.DeleteDatasetRequest(name=dataset_name,)
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        op = self.auto_ml_client.delete_dataset(request=request, **method_kwargs)
         self.__log_operation_info("Delete dataset", op)
         return op
 
@@ -730,7 +750,7 @@ def import_data(
         project=None,
         region=None,
         credentials=None,
-        **kwargs
+        **kwargs,
     ):
         """Imports data into a dataset.
 
@@ -814,7 +834,6 @@ def import_data(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
         request = {}
@@ -838,9 +857,12 @@ def import_data(
                 "One of 'gcs_input_uris', or 'bigquery_input_uri', or 'pandas_dataframe' must be set."
             )
 
-        op = self.auto_ml_client.import_data(
-            name=dataset_name, input_config=request, **kwargs
+        req = google.cloud.automl_v1beta1.ImportDataRequest(
+            name=dataset_name, input_config=request
         )
+        method_kwargs = self.__process_request_kwargs(req, **kwargs)
+
+        op = self.auto_ml_client.import_data(request=req, **method_kwargs)
         self.__log_operation_info("Data import", op)
         return op
 
@@ -853,7 +875,7 @@ def export_data(
         bigquery_output_uri=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Exports data from a dataset.
 
@@ -923,7 +945,6 @@ def export_data(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
         request = {}
@@ -936,9 +957,12 @@ def export_data(
                 "One of 'gcs_output_uri_prefix', or 'bigquery_output_uri' must be set."
             )
 
-        op = self.auto_ml_client.export_data(
+        req = google.cloud.automl_v1beta1.ExportDataRequest(
             name=dataset_name, output_config=request, **kwargs
         )
+
+        method_kwargs = self.__process_request_kwargs(req, **kwargs)
+        op = self.auto_ml_client.export_data(request=req, **method_kwargs)
         self.__log_operation_info("Export data", op)
         return op
 
@@ -980,7 +1004,10 @@ def get_table_spec(self, table_spec_name, project=None, region=None, **kwargs):
                 to a retryable error and retry attempts failed.
             ValueError: If required parameters are missing.
         """
-        return self.auto_ml_client.get_table_spec(name=table_spec_name, **kwargs)
+        request = google.cloud.automl_v1beta1.GetTableSpecRequest(name=table_spec_name,)
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+
+        return self.auto_ml_client.get_table_spec(request=request, **method_kwargs)
 
     def list_table_specs(
         self,
@@ -989,7 +1016,7 @@ def list_table_specs(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Lists table specs.
 
@@ -1049,10 +1076,13 @@ def list_table_specs(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
-        return self.auto_ml_client.list_table_specs(parent=dataset_name, **kwargs)
+        request = google.cloud.automl_v1beta1.ListTableSpecsRequest(
+            parent=dataset_name,
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.list_table_specs(request=request, **method_kwargs)
 
     def get_column_spec(self, column_spec_name, project=None, region=None, **kwargs):
         """Gets a single column spec in a particular project and region.
@@ -1092,7 +1122,11 @@ def get_column_spec(self, column_spec_name, project=None, region=None, **kwargs)
                 to a retryable error and retry attempts failed.
             ValueError: If required parameters are missing.
         """
-        return self.auto_ml_client.get_column_spec(name=column_spec_name, **kwargs)
+        request = google.cloud.automl_v1beta1.GetColumnSpecRequest(
+            name=column_spec_name,
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.get_column_spec(request=request, **method_kwargs)
 
     def list_column_specs(
         self,
@@ -1103,7 +1137,7 @@ def list_column_specs(
         table_spec_index=0,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Lists column specs.
 
@@ -1181,13 +1215,17 @@ def list_column_specs(
                     dataset_name=dataset_name,
                     project=project,
                     region=region,
-                    **kwargs
                 )
             ]
 
             table_spec_name = table_specs[table_spec_index].name
 
-        return self.auto_ml_client.list_column_specs(parent=table_spec_name, **kwargs)
+        request = google.cloud.automl_v1beta1.ListColumnSpecsRequest(
+            parent=table_spec_name,
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+
+        return self.auto_ml_client.list_column_specs(request=request, **method_kwargs)
 
     def update_column_spec(
         self,
@@ -1202,7 +1240,7 @@ def update_column_spec(
         nullable=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Updates a column's specs.
 
@@ -1292,7 +1330,6 @@ def update_column_spec(
             column_spec_display_name=column_spec_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
         # type code must always be set
@@ -1309,7 +1346,6 @@ def update_column_spec(
                     table_spec_index=table_spec_index,
                     project=project,
                     region=region,
-                    **kwargs
                 )
             }[column_spec_name].data_type.type_code
 
@@ -1317,11 +1353,14 @@ def update_column_spec(
         if nullable is not None:
             data_type["nullable"] = nullable
 
-        data_type["type_code"] = type_code
+        data_type["type_code"] = google.cloud.automl_v1beta1.TypeCode(type_code)
 
-        request = {"name": column_spec_name, "data_type": data_type}
+        request = google.cloud.automl_v1beta1.UpdateColumnSpecRequest(
+            column_spec={"name": column_spec_name, "data_type": data_type}
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
 
-        return self.auto_ml_client.update_column_spec(column_spec=request, **kwargs)
+        return self.auto_ml_client.update_column_spec(request=request, **method_kwargs)
 
     def set_target_column(
         self,
@@ -1334,7 +1373,7 @@ def set_target_column(
         column_spec_display_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Sets the target column for a given table.
 
@@ -1419,7 +1458,7 @@ def set_target_column(
             column_spec_display_name=column_spec_display_name,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
         column_spec_id = column_spec_name.rsplit("/", 1)[-1]
 
@@ -1429,16 +1468,19 @@ def set_target_column(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
         metadata = dataset.tables_dataset_metadata
         metadata = self.__update_metadata(
             metadata, "target_column_spec_id", column_spec_id
         )
 
-        request = {"name": dataset.name, "tables_dataset_metadata": metadata}
+        request = google.cloud.automl_v1beta1.UpdateDatasetRequest(
+            dataset={"name": dataset.name, "tables_dataset_metadata": metadata}
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
 
-        return self.auto_ml_client.update_dataset(dataset=request, **kwargs)
+        return self.auto_ml_client.update_dataset(request=request, **method_kwargs)
 
     def set_time_column(
         self,
@@ -1451,7 +1493,7 @@ def set_time_column(
         column_spec_display_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Sets the time column which designates which data will be of type
         timestamp and will be used for the timeseries data.
@@ -1534,7 +1576,7 @@ def set_time_column(
             column_spec_display_name=column_spec_display_name,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
         column_spec_id = column_spec_name.rsplit("/", 1)[-1]
 
@@ -1544,19 +1586,18 @@ def set_time_column(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
-        table_spec_full_id = self.__table_spec_name_from_args(
-            dataset_name=dataset_name, **kwargs
-        )
-
-        my_table_spec = {
-            "name": table_spec_full_id,
-            "time_column_spec_id": column_spec_id,
-        }
+        table_spec_full_id = self.__table_spec_name_from_args(dataset_name=dataset_name)
 
-        return self.auto_ml_client.update_table_spec(table_spec=my_table_spec, **kwargs)
+        request = google.cloud.automl_v1beta1.UpdateTableSpecRequest(
+            table_spec={
+                "name": table_spec_full_id,
+                "time_column_spec_id": column_spec_id,
+            }
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.update_table_spec(request=request, **method_kwargs)
 
     def clear_time_column(
         self,
@@ -1565,7 +1606,7 @@ def clear_time_column(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Clears the time column which designates which data will be of type
         timestamp and will be used for the timeseries data.
@@ -1627,16 +1668,17 @@ def clear_time_column(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
-        table_spec_full_id = self.__table_spec_name_from_args(
-            dataset_name=dataset_name, **kwargs
-        )
+        table_spec_full_id = self.__table_spec_name_from_args(dataset_name=dataset_name)
 
         my_table_spec = {"name": table_spec_full_id, "time_column_spec_id": None}
 
-        return self.auto_ml_client.update_table_spec(table_spec=my_table_spec, **kwargs)
+        request = google.cloud.automl_v1beta1.UpdateTableSpecRequest(
+            table_spec=my_table_spec
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.update_table_spec(request=request, **method_kwargs)
 
     def set_weight_column(
         self,
@@ -1649,7 +1691,7 @@ def set_weight_column(
         column_spec_display_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Sets the weight column for a given table.
 
@@ -1734,7 +1776,6 @@ def set_weight_column(
             column_spec_display_name=column_spec_display_name,
             project=project,
             region=region,
-            **kwargs
         )
         column_spec_id = column_spec_name.rsplit("/", 1)[-1]
 
@@ -1744,16 +1785,19 @@ def set_weight_column(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
         metadata = dataset.tables_dataset_metadata
         metadata = self.__update_metadata(
             metadata, "weight_column_spec_id", column_spec_id
         )
 
-        request = {"name": dataset.name, "tables_dataset_metadata": metadata}
+        request = google.cloud.automl_v1beta1.UpdateDatasetRequest(
+            dataset={"name": dataset.name, "tables_dataset_metadata": metadata}
+        )
+
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
 
-        return self.auto_ml_client.update_dataset(dataset=request, **kwargs)
+        return self.auto_ml_client.update_dataset(request=request, **method_kwargs)
 
     def clear_weight_column(
         self,
@@ -1762,7 +1806,7 @@ def clear_weight_column(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Clears the weight column for a given dataset.
 
@@ -1825,14 +1869,16 @@ def clear_weight_column(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
         )
         metadata = dataset.tables_dataset_metadata
         metadata = self.__update_metadata(metadata, "weight_column_spec_id", None)
 
-        request = {"name": dataset.name, "tables_dataset_metadata": metadata}
+        request = google.cloud.automl_v1beta1.UpdateDatasetRequest(
+            dataset={"name": dataset.name, "tables_dataset_metadata": metadata}
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
 
-        return self.auto_ml_client.update_dataset(dataset=request, **kwargs)
+        return self.auto_ml_client.update_dataset(request=request, **method_kwargs)
 
     def set_test_train_column(
         self,
@@ -1845,7 +1891,7 @@ def set_test_train_column(
         column_spec_display_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Sets the test/train (ml_use) column which designates which data
         belongs to the test and train sets. This column must be categorical.
@@ -1931,7 +1977,7 @@ def set_test_train_column(
             column_spec_display_name=column_spec_display_name,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
         column_spec_id = column_spec_name.rsplit("/", 1)[-1]
 
@@ -1941,16 +1987,19 @@ def set_test_train_column(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
         metadata = dataset.tables_dataset_metadata
         metadata = self.__update_metadata(
             metadata, "ml_use_column_spec_id", column_spec_id
         )
 
-        request = {"name": dataset.name, "tables_dataset_metadata": metadata}
+        request = google.cloud.automl_v1beta1.UpdateDatasetRequest(
+            dataset={"name": dataset.name, "tables_dataset_metadata": metadata}
+        )
 
-        return self.auto_ml_client.update_dataset(dataset=request, **kwargs)
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.update_dataset(request=request, **method_kwargs)
 
     def clear_test_train_column(
         self,
@@ -1959,7 +2008,7 @@ def clear_test_train_column(
         dataset_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Clears the test/train (ml_use) column which designates which data
         belongs to the test and train sets.
@@ -2023,14 +2072,17 @@ def clear_test_train_column(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
         metadata = dataset.tables_dataset_metadata
         metadata = self.__update_metadata(metadata, "ml_use_column_spec_id", None)
 
-        request = {"name": dataset.name, "tables_dataset_metadata": metadata}
+        request = google.cloud.automl_v1beta1.UpdateDatasetRequest(
+            dataset={"name": dataset.name, "tables_dataset_metadata": metadata}
+        )
 
-        return self.auto_ml_client.update_dataset(dataset=request, **kwargs)
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.update_dataset(request=request, **method_kwargs)
 
     def list_models(self, project=None, region=None, **kwargs):
         """List all models in a particular project and region.
@@ -2074,10 +2126,14 @@ def list_models(self, project=None, region=None, **kwargs):
                 to a retryable error and retry attempts failed.
             ValueError: If required parameters are missing.
         """
-        return self.auto_ml_client.list_models(
-            parent=self.__location_path(project=project, region=region), **kwargs
+
+        request = google.cloud.automl_v1beta1.ListModelsRequest(
+            parent=self.__location_path(project=project, region=region),
         )
 
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.list_models(request=request, **method_kwargs)
+
     def list_model_evaluations(
         self,
         project=None,
@@ -2085,7 +2141,7 @@ def list_model_evaluations(
         model=None,
         model_display_name=None,
         model_name=None,
-        **kwargs
+        **kwargs,
     ):
         """List all model evaluations for a given model.
 
@@ -2153,10 +2209,15 @@ def list_model_evaluations(
             model_display_name=model_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
-        return self.auto_ml_client.list_model_evaluations(parent=model_name, **kwargs)
+        request = google.cloud.automl_v1beta1.ListModelEvaluationsRequest(
+            parent=model_name,
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.auto_ml_client.list_model_evaluations(
+            request=request, **method_kwargs
+        )
 
     def create_model(
         self,
@@ -2172,7 +2233,7 @@ def create_model(
         include_column_spec_names=None,
         exclude_column_spec_names=None,
         disable_early_stopping=False,
-        **kwargs
+        **kwargs,
     ):
         """Create a model. This will train your model on the given dataset.
 
@@ -2276,7 +2337,7 @@ def create_model(
             dataset_display_name=dataset_display_name,
             project=project,
             region=region,
-            **kwargs
+            **kwargs,
         )
 
         model_metadata["train_budget_milli_node_hours"] = train_budget_milli_node_hours
@@ -2292,7 +2353,7 @@ def create_model(
                 dataset=dataset,
                 dataset_name=dataset_name,
                 dataset_display_name=dataset_display_name,
-                **kwargs
+                **kwargs,
             )
         ]
 
@@ -2310,17 +2371,19 @@ def create_model(
 
             model_metadata["input_feature_column_specs"] = final_columns
 
-        request = {
-            "display_name": model_display_name,
-            "dataset_id": dataset_id,
-            "tables_model_metadata": model_metadata,
-        }
-
-        op = self.auto_ml_client.create_model(
+        req = google.cloud.automl_v1beta1.CreateModelRequest(
             parent=self.__location_path(project=project, region=region),
-            model=request,
-            **kwargs
+            model=google.cloud.automl_v1beta1.Model(
+                display_name=model_display_name,
+                dataset_id=dataset_id,
+                tables_model_metadata=google.cloud.automl_v1beta1.TablesModelMetadata(
+                    model_metadata
+                ),
+            ),
         )
+
+        method_kwargs = self.__process_request_kwargs(req, **kwargs)
+        op = self.auto_ml_client.create_model(request=req, **method_kwargs)
         self.__log_operation_info("Model creation", op)
         return op
 
@@ -2331,7 +2394,7 @@ def delete_model(
         model_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Deletes a model. Note this will not delete any datasets associated
         with this model.
@@ -2391,13 +2454,14 @@ def delete_model(
                 model_display_name=model_display_name,
                 project=project,
                 region=region,
-                **kwargs
             )
         # delete is idempotent
         except exceptions.NotFound:
             return None
 
-        op = self.auto_ml_client.delete_model(name=model_name, **kwargs)
+        request = google.cloud.automl_v1beta1.DeleteModelRequest(name=model_name)
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        op = self.auto_ml_client.delete_model(request=request, **method_kwargs)
         self.__log_operation_info("Delete model", op)
         return op
 
@@ -2441,8 +2505,12 @@ def get_model_evaluation(
                 to a retryable error and retry attempts failed.
             ValueError: If required parameters are missing.
         """
+        request = google.cloud.automl_v1beta1.GetModelEvaluationRequest(
+            name=model_evaluation_name
+        )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
         return self.auto_ml_client.get_model_evaluation(
-            name=model_evaluation_name, **kwargs
+            request=request, **method_kwargs
         )
 
     def get_model(
@@ -2451,7 +2519,7 @@ def get_model(
         region=None,
         model_name=None,
         model_display_name=None,
-        **kwargs
+        **kwargs,
     ):
         """Gets a single model in a particular project and region.
 
@@ -2503,10 +2571,10 @@ def get_model(
             )
 
         if model_name is not None:
-            return self.auto_ml_client.get_model(name=model_name, **kwargs)
+            return self.auto_ml_client.get_model(name=model_name)
 
         return self.__lookup_by_display_name(
-            "model", self.list_models(project, region, **kwargs), model_display_name
+            "model", self.list_models(project, region), model_display_name
         )
 
     # TODO(jonathanskim): allow deployment from just model ID
@@ -2517,7 +2585,7 @@ def deploy_model(
         model_display_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Deploys a model. This allows you make online predictions using the
         model you've deployed.
@@ -2576,10 +2644,12 @@ def deploy_model(
             model_display_name=model_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
-        op = self.auto_ml_client.deploy_model(name=model_name, **kwargs)
+        request = google.cloud.automl_v1beta1.DeployModelRequest(name=model_name)
+
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        op = self.auto_ml_client.deploy_model(request=request, **method_kwargs)
         self.__log_operation_info("Deploy model", op)
         return op
 
@@ -2590,7 +2660,7 @@ def undeploy_model(
         model_display_name=None,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Undeploys a model.
 
@@ -2648,10 +2718,11 @@ def undeploy_model(
             model_display_name=model_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
-        op = self.auto_ml_client.undeploy_model(name=model_name, **kwargs)
+        request = google.cloud.automl_v1beta1.UndeployModelRequest(name=model_name)
+        method_kwargs = self.__process_request_kwargs(request=request, **kwargs)
+        op = self.auto_ml_client.undeploy_model(request=request, **method_kwargs)
         self.__log_operation_info("Undeploy model", op)
         return op
 
@@ -2665,7 +2736,7 @@ def predict(
         feature_importance=False,
         project=None,
         region=None,
-        **kwargs
+        **kwargs,
     ):
         """Makes a prediction on a deployed model. This will fail if the model
         was not deployed.
@@ -2730,7 +2801,6 @@ def predict(
             model_display_name=model_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
         column_specs = model.tables_model_metadata.input_feature_column_specs
@@ -2766,9 +2836,11 @@ def predict(
         if feature_importance:
             params = {"feature_importance": "true"}
 
-        return self.prediction_client.predict(
-            name=model.name, payload=payload, params=params, **kwargs
+        request = google.cloud.automl_v1beta1.PredictRequest(
+            name=model.name, payload=payload, params=params,
         )
+        method_kwargs = self.__process_request_kwargs(request, **kwargs)
+        return self.prediction_client.predict(request=request, **method_kwargs)
 
     def batch_predict(
         self,
@@ -2785,7 +2857,7 @@ def batch_predict(
         credentials=None,
         inputs=None,
         params={},
-        **kwargs
+        **kwargs,
     ):
         """Makes a batch prediction on a model. This does _not_ require the
         model to be deployed.
@@ -2873,7 +2945,6 @@ def batch_predict(
             model_display_name=model_display_name,
             project=project,
             region=region,
-            **kwargs
         )
 
         input_request = None
@@ -2911,11 +2982,11 @@ def batch_predict(
                 "One of 'gcs_output_uri_prefix'/'bigquery_output_uri' must be set"
             )
 
-        op = self.prediction_client.batch_predict(
-            name=model_name,
-            input_config=input_request,
-            output_config=output_request,
-            **kwargs
+        req = google.cloud.automl_v1beta1.BatchPredictRequest(
+            name=model_name, input_config=input_request, output_config=output_request,
         )
+
+        method_kwargs = self.__process_request_kwargs(req, **kwargs)
+        op = self.prediction_client.batch_predict(request=req, **method_kwargs)
         self.__log_operation_info("Batch predict", op)
         return op
diff --git a/noxfile.py b/noxfile.py
index 62ae86d4..9c69b3b7 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -74,6 +74,7 @@ def default(session):
 
     session.install("mock", "pytest", "pytest-cov")
     session.install("-e", ".[pandas,storage]")
+    session.install("proto-plus==1.8.1")
 
     # Run py.test against the unit tests.
     session.run(
diff --git a/samples/tables/automl_tables_dataset.py b/samples/tables/automl_tables_dataset.py
index 144f2ee6..76ece1c9 100644
--- a/samples/tables/automl_tables_dataset.py
+++ b/samples/tables/automl_tables_dataset.py
@@ -47,9 +47,7 @@ def create_dataset(project_id, compute_region, dataset_display_name):
     print("Dataset metadata:")
     print("\t{}".format(dataset.tables_dataset_metadata))
     print("Dataset example count: {}".format(dataset.example_count))
-    print("Dataset create time:")
-    print("\tseconds: {}".format(dataset.create_time.seconds))
-    print("\tnanos: {}".format(dataset.create_time.nanos))
+    print("Dataset create time: {}".format(dataset.create_time))
 
     # [END automl_tables_create_dataset]
 
@@ -105,9 +103,7 @@ def list_datasets(project_id, compute_region, filter_=None):
             )
         )
         print("Dataset example count: {}".format(dataset.example_count))
-        print("Dataset create time:")
-        print("\tseconds: {}".format(dataset.create_time.seconds))
-        print("\tnanos: {}".format(dataset.create_time.nanos))
+        print("Dataset create time: {}".format(dataset.create_time))
         print("\n")
 
         # [END automl_tables_list_datasets]
@@ -137,9 +133,7 @@ def get_dataset(project_id, compute_region, dataset_display_name):
     print("Dataset metadata:")
     print("\t{}".format(dataset.tables_dataset_metadata))
     print("Dataset example count: {}".format(dataset.example_count))
-    print("Dataset create time:")
-    print("\tseconds: {}".format(dataset.create_time.seconds))
-    print("\tnanos: {}".format(dataset.create_time.nanos))
+    print("Dataset create time: {}".format(dataset.create_time))
 
     return dataset
 
diff --git a/samples/tables/automl_tables_model.py b/samples/tables/automl_tables_model.py
index 09a8f4ca..95dc5eb8 100644
--- a/samples/tables/automl_tables_model.py
+++ b/samples/tables/automl_tables_model.py
@@ -190,7 +190,7 @@ def get_model(project_id, compute_region, model_display_name):
 
 
 def list_model_evaluations(
-    project_id, compute_region, model_display_name, filter_=None
+    project_id, compute_region, model_display_name, filter=None
 ):
 
     """List model evaluations."""
@@ -200,7 +200,7 @@ def list_model_evaluations(
     # project_id = 'PROJECT_ID_HERE'
     # compute_region = 'COMPUTE_REGION_HERE'
     # model_display_name = 'MODEL_DISPLAY_NAME_HERE'
-    # filter_ = 'filter expression here'
+    # filter = 'filter expression here'
 
     from google.cloud import automl_v1beta1 as automl
 
@@ -208,7 +208,7 @@ def list_model_evaluations(
 
     # List all the model evaluations in the model by applying filter.
     response = client.list_model_evaluations(
-        model_display_name=model_display_name, filter_=filter_
+        model_display_name=model_display_name, filter=filter
     )
 
     print("List of model evaluations:")
@@ -220,9 +220,7 @@ def list_model_evaluations(
                 evaluation.evaluated_example_count
             )
         )
-        print("Model evaluation time:")
-        print("\tseconds: {}".format(evaluation.create_time.seconds))
-        print("\tnanos: {}".format(evaluation.create_time.nanos))
+        print("Model evaluation time: {}".format(evaluation.create_time))
         print("\n")
         # [END automl_tables_list_model_evaluations]
         result.append(evaluation)
@@ -261,7 +259,7 @@ def get_model_evaluation(
 
 
 def display_evaluation(
-    project_id, compute_region, model_display_name, filter_=None
+    project_id, compute_region, model_display_name, filter=None
 ):
     """Display evaluation."""
     # [START automl_tables_display_evaluation]
@@ -269,7 +267,7 @@ def display_evaluation(
     # project_id = 'PROJECT_ID_HERE'
     # compute_region = 'COMPUTE_REGION_HERE'
     # model_display_name = 'MODEL_DISPLAY_NAME_HERE'
-    # filter_ = 'filter expression here'
+    # filter = 'filter expression here'
 
     from google.cloud import automl_v1beta1 as automl
 
@@ -277,7 +275,7 @@ def display_evaluation(
 
     # List all the model evaluations in the model by applying filter.
     response = client.list_model_evaluations(
-        model_display_name=model_display_name, filter_=filter_
+        model_display_name=model_display_name, filter=filter
     )
 
     # Iterate through the results.
diff --git a/samples/tables/automl_tables_predict.py b/samples/tables/automl_tables_predict.py
index 9787e1b9..a330213c 100644
--- a/samples/tables/automl_tables_predict.py
+++ b/samples/tables/automl_tables_predict.py
@@ -58,7 +58,7 @@ def predict(
     print("Prediction results:")
     for result in response.payload:
         print(
-            "Predicted class name: {}".format(result.tables.value.string_value)
+            "Predicted class name: {}".format(result.tables.value)
         )
         print("Predicted class score: {}".format(result.tables.score))
 
diff --git a/samples/tables/endpoint_test.py b/samples/tables/endpoint_test.py
index 5a20aba5..6af6b8da 100644
--- a/samples/tables/endpoint_test.py
+++ b/samples/tables/endpoint_test.py
@@ -23,4 +23,4 @@
 def test_client_creation(capsys):
     automl_tables_set_endpoint.create_client_with_endpoint(PROJECT)
     out, _ = capsys.readouterr()
-    assert "GRPCIterator" in out
+    assert "ListDatasetsPager" in out
diff --git a/tests/unit/test_tables_client_v1beta1.py b/tests/unit/test_tables_client_v1beta1.py
index 3c5b55d8..1d5b168c 100644
--- a/tests/unit/test_tables_client_v1beta1.py
+++ b/tests/unit/test_tables_client_v1beta1.py
@@ -48,29 +48,31 @@ def tables_client(
 
     def test_list_datasets_empty(self):
         client = self.tables_client(
-            {
+            client_attrs={
                 "list_datasets.return_value": [],
                 "location_path.return_value": LOCATION_PATH,
             },
-            {},
+            prediction_client_attrs={},
         )
         ds = client.list_datasets()
-        client.auto_ml_client.location_path.assert_called_with(PROJECT, REGION)
-        client.auto_ml_client.list_datasets.assert_called_with(parent=LOCATION_PATH)
+
+        request = automl_v1beta1.ListDatasetsRequest(parent=LOCATION_PATH)
+        client.auto_ml_client.list_datasets.assert_called_with(request=request)
         assert ds == []
 
     def test_list_datasets_not_empty(self):
         datasets = ["some_dataset"]
         client = self.tables_client(
-            {
+            client_attrs={
                 "list_datasets.return_value": datasets,
                 "location_path.return_value": LOCATION_PATH,
             },
-            {},
+            prediction_client_attrs={},
         )
         ds = client.list_datasets()
-        client.auto_ml_client.location_path.assert_called_with(PROJECT, REGION)
-        client.auto_ml_client.list_datasets.assert_called_with(parent=LOCATION_PATH)
+
+        request = automl_v1beta1.ListDatasetsRequest(parent=LOCATION_PATH)
+        client.auto_ml_client.list_datasets.assert_called_with(request=request)
         assert len(ds) == 1
         assert ds[0] == "some_dataset"
 
@@ -84,7 +86,9 @@ def test_get_dataset_name(self):
         dataset_actual = "dataset"
         client = self.tables_client({"get_dataset.return_value": dataset_actual}, {})
         dataset = client.get_dataset(dataset_name="my_dataset")
-        client.auto_ml_client.get_dataset.assert_called_with(name="my_dataset")
+        client.auto_ml_client.get_dataset.assert_called_with(
+            request=automl_v1beta1.GetDatasetRequest(name="my_dataset")
+        )
         assert dataset == dataset_actual
 
     def test_get_no_dataset(self):
@@ -93,7 +97,9 @@ def test_get_no_dataset(self):
         )
         with pytest.raises(exceptions.NotFound):
             client.get_dataset(dataset_name="my_dataset")
-        client.auto_ml_client.get_dataset.assert_called_with(name="my_dataset")
+        client.auto_ml_client.get_dataset.assert_called_with(
+            request=automl_v1beta1.GetDatasetRequest(name="my_dataset")
+        )
 
     def test_get_dataset_from_empty_list(self):
         client = self.tables_client({"list_datasets.return_value": []}, {})
@@ -142,12 +148,14 @@ def test_create_dataset(self):
             },
             {},
         )
-        metadata = {"metadata": "values"}
+        metadata = {"primary_table_spec_id": "1234"}
         dataset = client.create_dataset("name", metadata=metadata)
-        client.auto_ml_client.location_path.assert_called_with(PROJECT, REGION)
+
         client.auto_ml_client.create_dataset.assert_called_with(
-            parent=LOCATION_PATH,
-            dataset={"display_name": "name", "tables_dataset_metadata": metadata},
+            request=automl_v1beta1.CreateDatasetRequest(
+                parent=LOCATION_PATH,
+                dataset={"display_name": "name", "tables_dataset_metadata": metadata},
+            )
         )
         assert dataset.display_name == "name"
 
@@ -156,7 +164,9 @@ def test_delete_dataset(self):
         dataset.configure_mock(name="name")
         client = self.tables_client({"delete_dataset.return_value": None}, {})
         client.delete_dataset(dataset=dataset)
-        client.auto_ml_client.delete_dataset.assert_called_with(name="name")
+        client.auto_ml_client.delete_dataset.assert_called_with(
+            request=automl_v1beta1.DeleteDatasetRequest(name="name")
+        )
 
     def test_delete_dataset_not_found(self):
         client = self.tables_client({"list_datasets.return_value": []}, {})
@@ -166,7 +176,9 @@ def test_delete_dataset_not_found(self):
     def test_delete_dataset_name(self):
         client = self.tables_client({"delete_dataset.return_value": None}, {})
         client.delete_dataset(dataset_name="name")
-        client.auto_ml_client.delete_dataset.assert_called_with(name="name")
+        client.auto_ml_client.delete_dataset.assert_called_with(
+            request=automl_v1beta1.DeleteDatasetRequest(name="name")
+        )
 
     def test_export_not_found(self):
         client = self.tables_client({"list_datasets.return_value": []}, {})
@@ -179,14 +191,20 @@ def test_export_gcs_uri(self):
         client = self.tables_client({"export_data.return_value": None}, {})
         client.export_data(dataset_name="name", gcs_output_uri_prefix="uri")
         client.auto_ml_client.export_data.assert_called_with(
-            name="name", output_config={"gcs_destination": {"output_uri_prefix": "uri"}}
+            request=automl_v1beta1.ExportDataRequest(
+                name="name",
+                output_config={"gcs_destination": {"output_uri_prefix": "uri"}},
+            )
         )
 
     def test_export_bq_uri(self):
         client = self.tables_client({"export_data.return_value": None}, {})
         client.export_data(dataset_name="name", bigquery_output_uri="uri")
         client.auto_ml_client.export_data.assert_called_with(
-            name="name", output_config={"bigquery_destination": {"output_uri": "uri"}}
+            request=automl_v1beta1.ExportDataRequest(
+                name="name",
+                output_config={"bigquery_destination": {"output_uri": "uri"}},
+            )
         )
 
     def test_import_not_found(self):
@@ -213,7 +231,9 @@ def test_import_pandas_dataframe(self):
         client.gcs_client.ensure_bucket_exists.assert_called_with(PROJECT, REGION)
         client.gcs_client.upload_pandas_dataframe.assert_called_with(dataframe)
         client.auto_ml_client.import_data.assert_called_with(
-            name="name", input_config={"gcs_source": {"input_uris": ["uri"]}}
+            request=automl_v1beta1.ImportDataRequest(
+                name="name", input_config={"gcs_source": {"input_uris": ["uri"]}}
+            )
         )
 
     def test_import_pandas_dataframe_init_gcs(self):
@@ -240,34 +260,44 @@ def test_import_pandas_dataframe_init_gcs(self):
             client.gcs_client.ensure_bucket_exists.assert_called_with(PROJECT, REGION)
             client.gcs_client.upload_pandas_dataframe.assert_called_with(dataframe)
             client.auto_ml_client.import_data.assert_called_with(
-                name="name", input_config={"gcs_source": {"input_uris": ["uri"]}}
+                request=automl_v1beta1.ImportDataRequest(
+                    name="name", input_config={"gcs_source": {"input_uris": ["uri"]}}
+                )
             )
 
     def test_import_gcs_uri(self):
         client = self.tables_client({"import_data.return_value": None}, {})
         client.import_data(dataset_name="name", gcs_input_uris="uri")
         client.auto_ml_client.import_data.assert_called_with(
-            name="name", input_config={"gcs_source": {"input_uris": ["uri"]}}
+            request=automl_v1beta1.ImportDataRequest(
+                name="name", input_config={"gcs_source": {"input_uris": ["uri"]}}
+            )
         )
 
     def test_import_gcs_uris(self):
         client = self.tables_client({"import_data.return_value": None}, {})
         client.import_data(dataset_name="name", gcs_input_uris=["uri", "uri"])
         client.auto_ml_client.import_data.assert_called_with(
-            name="name", input_config={"gcs_source": {"input_uris": ["uri", "uri"]}}
+            request=automl_v1beta1.ImportDataRequest(
+                name="name", input_config={"gcs_source": {"input_uris": ["uri", "uri"]}}
+            )
         )
 
     def test_import_bq_uri(self):
         client = self.tables_client({"import_data.return_value": None}, {})
         client.import_data(dataset_name="name", bigquery_input_uri="uri")
         client.auto_ml_client.import_data.assert_called_with(
-            name="name", input_config={"bigquery_source": {"input_uri": "uri"}}
+            request=automl_v1beta1.ImportDataRequest(
+                name="name", input_config={"bigquery_source": {"input_uri": "uri"}}
+            )
         )
 
     def test_list_table_specs(self):
         client = self.tables_client({"list_table_specs.return_value": None}, {})
         client.list_table_specs(dataset_name="name")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
 
     def test_list_table_specs_not_found(self):
         client = self.tables_client(
@@ -275,17 +305,23 @@ def test_list_table_specs_not_found(self):
         )
         with pytest.raises(exceptions.NotFound):
             client.list_table_specs(dataset_name="name")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
 
     def test_get_table_spec(self):
         client = self.tables_client({}, {})
         client.get_table_spec("name")
-        client.auto_ml_client.get_table_spec.assert_called_with(name="name")
+        client.auto_ml_client.get_table_spec.assert_called_with(
+            request=automl_v1beta1.GetTableSpecRequest(name="name")
+        )
 
     def test_get_column_spec(self):
         client = self.tables_client({}, {})
         client.get_column_spec("name")
-        client.auto_ml_client.get_column_spec.assert_called_with(name="name")
+        client.auto_ml_client.get_column_spec.assert_called_with(
+            request=automl_v1beta1.GetColumnSpecRequest(name="name")
+        )
 
     def test_list_column_specs(self):
         table_spec_mock = mock.Mock()
@@ -299,171 +335,238 @@ def test_list_column_specs(self):
             {},
         )
         client.list_column_specs(dataset_name="name")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
 
     def test_update_column_spec_not_found(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column", display_name="column", data_type=data_type_mock
+
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(type_code=automl_v1beta1.TypeCode.STRING),
         )
+
         client = self.tables_client(
-            {
+            client_attrs={
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
-            {},
+            prediction_client_attrs={},
         )
         with pytest.raises(exceptions.NotFound):
             client.update_column_spec(dataset_name="name", column_spec_name="column2")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_column_spec.assert_not_called()
 
     def test_update_column_spec_display_name_not_found(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column", display_name="column", data_type=data_type_mock
+
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(type_code=automl_v1beta1.TypeCode.STRING),
         )
         client = self.tables_client(
-            {
+            client_attrs={
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
-            {},
+            prediction_client_attrs={},
         )
         with pytest.raises(exceptions.NotFound):
             client.update_column_spec(
                 dataset_name="name", column_spec_display_name="column2"
             )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_column_spec.assert_not_called()
 
     def test_update_column_spec_name_no_args(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column/2", display_name="column", data_type=data_type_mock
+
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column/2",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(
+                type_code=automl_v1beta1.TypeCode.FLOAT64
+            ),
         )
+
         client = self.tables_client(
             {
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
             {},
         )
         client.update_column_spec(dataset_name="name", column_spec_name="column/2")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_column_spec.assert_called_with(
-            column_spec={"name": "column/2", "data_type": {"type_code": "type_code"}}
+            request=automl_v1beta1.UpdateColumnSpecRequest(
+                column_spec={
+                    "name": "column/2",
+                    "data_type": {"type_code": automl_v1beta1.TypeCode.FLOAT64},
+                }
+            )
         )
 
     def test_update_column_spec_no_args(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column", display_name="column", data_type=data_type_mock
+
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(
+                type_code=automl_v1beta1.TypeCode.FLOAT64
+            ),
         )
+
         client = self.tables_client(
             {
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
             {},
         )
         client.update_column_spec(
             dataset_name="name", column_spec_display_name="column"
         )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_column_spec.assert_called_with(
-            column_spec={"name": "column", "data_type": {"type_code": "type_code"}}
+            request=automl_v1beta1.UpdateColumnSpecRequest(
+                column_spec={
+                    "name": "column",
+                    "data_type": {"type_code": automl_v1beta1.TypeCode.FLOAT64},
+                }
+            )
         )
 
     def test_update_column_spec_nullable(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column", display_name="column", data_type=data_type_mock
+
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(
+                type_code=automl_v1beta1.TypeCode.FLOAT64
+            ),
         )
+
         client = self.tables_client(
             {
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
             {},
         )
         client.update_column_spec(
             dataset_name="name", column_spec_display_name="column", nullable=True
         )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_column_spec.assert_called_with(
-            column_spec={
-                "name": "column",
-                "data_type": {"type_code": "type_code", "nullable": True},
-            }
+            request=automl_v1beta1.UpdateColumnSpecRequest(
+                column_spec={
+                    "name": "column",
+                    "data_type": {
+                        "type_code": automl_v1beta1.TypeCode.FLOAT64,
+                        "nullable": True,
+                    },
+                }
+            )
         )
 
     def test_update_column_spec_type_code(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column", display_name="column", data_type=data_type_mock
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(
+                type_code=automl_v1beta1.TypeCode.FLOAT64
+            ),
         )
         client = self.tables_client(
             {
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
             {},
         )
         client.update_column_spec(
             dataset_name="name",
             column_spec_display_name="column",
-            type_code="type_code2",
+            type_code=automl_v1beta1.TypeCode.ARRAY,
+        )
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
         )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
         client.auto_ml_client.update_column_spec.assert_called_with(
-            column_spec={"name": "column", "data_type": {"type_code": "type_code2"}}
+            request=automl_v1beta1.UpdateColumnSpecRequest(
+                column_spec={
+                    "name": "column",
+                    "data_type": {"type_code": automl_v1beta1.TypeCode.ARRAY},
+                }
+            )
         )
 
     def test_update_column_spec_type_code_nullable(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column", display_name="column", data_type=data_type_mock
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(
+                type_code=automl_v1beta1.TypeCode.FLOAT64
+            ),
         )
         client = self.tables_client(
             {
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
             {},
         )
@@ -471,30 +574,41 @@ def test_update_column_spec_type_code_nullable(self):
             dataset_name="name",
             nullable=True,
             column_spec_display_name="column",
-            type_code="type_code2",
+            type_code=automl_v1beta1.TypeCode.ARRAY,
+        )
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
         )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
         client.auto_ml_client.update_column_spec.assert_called_with(
-            column_spec={
-                "name": "column",
-                "data_type": {"type_code": "type_code2", "nullable": True},
-            }
+            request=automl_v1beta1.UpdateColumnSpecRequest(
+                column_spec={
+                    "name": "column",
+                    "data_type": {
+                        "type_code": automl_v1beta1.TypeCode.ARRAY,
+                        "nullable": True,
+                    },
+                }
+            )
         )
 
     def test_update_column_spec_type_code_nullable_false(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock = mock.Mock()
-        data_type_mock = mock.Mock(type_code="type_code")
-        column_spec_mock.configure_mock(
-            name="column", display_name="column", data_type=data_type_mock
+        column_spec = automl_v1beta1.ColumnSpec(
+            name="column",
+            display_name="column",
+            data_type=automl_v1beta1.DataType(
+                type_code=automl_v1beta1.TypeCode.FLOAT64
+            ),
         )
         client = self.tables_client(
             {
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [column_spec_mock],
+                "list_column_specs.return_value": [column_spec],
             },
             {},
         )
@@ -502,15 +616,24 @@ def test_update_column_spec_type_code_nullable_false(self):
             dataset_name="name",
             nullable=False,
             column_spec_display_name="column",
-            type_code="type_code2",
+            type_code=automl_v1beta1.TypeCode.FLOAT64,
+        )
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
         )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
         client.auto_ml_client.update_column_spec.assert_called_with(
-            column_spec={
-                "name": "column",
-                "data_type": {"type_code": "type_code2", "nullable": False},
-            }
+            request=automl_v1beta1.UpdateColumnSpecRequest(
+                column_spec={
+                    "name": "column",
+                    "data_type": {
+                        "type_code": automl_v1beta1.TypeCode.FLOAT64,
+                        "nullable": False,
+                    },
+                }
+            )
         )
 
     def test_set_target_column_table_not_found(self):
@@ -521,7 +644,9 @@ def test_set_target_column_table_not_found(self):
             client.set_target_column(
                 dataset_name="name", column_spec_display_name="column2"
             )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
         client.auto_ml_client.list_column_specs.assert_not_called()
         client.auto_ml_client.update_dataset.assert_not_called()
 
@@ -542,8 +667,12 @@ def test_set_target_column_not_found(self):
             client.set_target_column(
                 dataset_name="name", column_spec_display_name="column2"
             )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_dataset.assert_not_called()
 
     def test_set_target_column(self):
@@ -571,17 +700,23 @@ def test_set_target_column(self):
             {},
         )
         client.set_target_column(dataset_name="name", column_spec_display_name="column")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_dataset.assert_called_with(
-            dataset={
-                "name": "dataset",
-                "tables_dataset_metadata": {
-                    "target_column_spec_id": "1",
-                    "weight_column_spec_id": "2",
-                    "ml_use_column_spec_id": "3",
-                },
-            }
+            request=automl_v1beta1.UpdateDatasetRequest(
+                dataset={
+                    "name": "dataset",
+                    "tables_dataset_metadata": {
+                        "target_column_spec_id": "1",
+                        "weight_column_spec_id": "2",
+                        "ml_use_column_spec_id": "3",
+                    },
+                }
+            )
         )
 
     def test_set_weight_column_table_not_found(self):
@@ -594,7 +729,9 @@ def test_set_weight_column_table_not_found(self):
             )
         except exceptions.NotFound:
             pass
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
         client.auto_ml_client.list_column_specs.assert_not_called()
         client.auto_ml_client.update_dataset.assert_not_called()
 
@@ -615,8 +752,12 @@ def test_set_weight_column_not_found(self):
             client.set_weight_column(
                 dataset_name="name", column_spec_display_name="column2"
             )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_dataset.assert_not_called()
 
     def test_set_weight_column(self):
@@ -644,17 +785,23 @@ def test_set_weight_column(self):
             {},
         )
         client.set_weight_column(dataset_name="name", column_spec_display_name="column")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_dataset.assert_called_with(
-            dataset={
-                "name": "dataset",
-                "tables_dataset_metadata": {
-                    "target_column_spec_id": "1",
-                    "weight_column_spec_id": "2",
-                    "ml_use_column_spec_id": "3",
-                },
-            }
+            request=automl_v1beta1.UpdateDatasetRequest(
+                dataset={
+                    "name": "dataset",
+                    "tables_dataset_metadata": {
+                        "target_column_spec_id": "1",
+                        "weight_column_spec_id": "2",
+                        "ml_use_column_spec_id": "3",
+                    },
+                }
+            )
         )
 
     def test_clear_weight_column(self):
@@ -671,14 +818,16 @@ def test_clear_weight_column(self):
         client = self.tables_client({"get_dataset.return_value": dataset_mock}, {})
         client.clear_weight_column(dataset_name="name")
         client.auto_ml_client.update_dataset.assert_called_with(
-            dataset={
-                "name": "dataset",
-                "tables_dataset_metadata": {
-                    "target_column_spec_id": "1",
-                    "weight_column_spec_id": None,
-                    "ml_use_column_spec_id": "3",
-                },
-            }
+            request=automl_v1beta1.UpdateDatasetRequest(
+                dataset={
+                    "name": "dataset",
+                    "tables_dataset_metadata": {
+                        "target_column_spec_id": "1",
+                        "weight_column_spec_id": None,
+                        "ml_use_column_spec_id": "3",
+                    },
+                }
+            )
         )
 
     def test_set_test_train_column_table_not_found(self):
@@ -689,7 +838,9 @@ def test_set_test_train_column_table_not_found(self):
             client.set_test_train_column(
                 dataset_name="name", column_spec_display_name="column2"
             )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
         client.auto_ml_client.list_column_specs.assert_not_called()
         client.auto_ml_client.update_dataset.assert_not_called()
 
@@ -710,8 +861,12 @@ def test_set_test_train_column_not_found(self):
             client.set_test_train_column(
                 dataset_name="name", column_spec_display_name="column2"
             )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_dataset.assert_not_called()
 
     def test_set_test_train_column(self):
@@ -741,17 +896,23 @@ def test_set_test_train_column(self):
         client.set_test_train_column(
             dataset_name="name", column_spec_display_name="column"
         )
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_dataset.assert_called_with(
-            dataset={
-                "name": "dataset",
-                "tables_dataset_metadata": {
-                    "target_column_spec_id": "1",
-                    "weight_column_spec_id": "2",
-                    "ml_use_column_spec_id": "3",
-                },
-            }
+            request=automl_v1beta1.UpdateDatasetRequest(
+                dataset={
+                    "name": "dataset",
+                    "tables_dataset_metadata": {
+                        "target_column_spec_id": "1",
+                        "weight_column_spec_id": "2",
+                        "ml_use_column_spec_id": "3",
+                    },
+                }
+            )
         )
 
     def test_clear_test_train_column(self):
@@ -768,14 +929,16 @@ def test_clear_test_train_column(self):
         client = self.tables_client({"get_dataset.return_value": dataset_mock}, {})
         client.clear_test_train_column(dataset_name="name")
         client.auto_ml_client.update_dataset.assert_called_with(
-            dataset={
-                "name": "dataset",
-                "tables_dataset_metadata": {
-                    "target_column_spec_id": "1",
-                    "weight_column_spec_id": "2",
-                    "ml_use_column_spec_id": None,
-                },
-            }
+            request=automl_v1beta1.UpdateDatasetRequest(
+                dataset={
+                    "name": "dataset",
+                    "tables_dataset_metadata": {
+                        "target_column_spec_id": "1",
+                        "weight_column_spec_id": "2",
+                        "ml_use_column_spec_id": None,
+                    },
+                }
+            )
         )
 
     def test_set_time_column(self):
@@ -795,10 +958,16 @@ def test_set_time_column(self):
             {},
         )
         client.set_time_column(dataset_name="name", column_spec_display_name="column")
-        client.auto_ml_client.list_table_specs.assert_called_with(parent="name")
-        client.auto_ml_client.list_column_specs.assert_called_with(parent="table")
+        client.auto_ml_client.list_table_specs.assert_called_with(
+            request=automl_v1beta1.ListTableSpecsRequest(parent="name")
+        )
+        client.auto_ml_client.list_column_specs.assert_called_with(
+            request=automl_v1beta1.ListColumnSpecsRequest(parent="table")
+        )
         client.auto_ml_client.update_table_spec.assert_called_with(
-            table_spec={"name": "table", "time_column_spec_id": "3"}
+            request=automl_v1beta1.UpdateTableSpecRequest(
+                table_spec={"name": "table", "time_column_spec_id": "3"}
+            )
         )
 
     def test_clear_time_column(self):
@@ -816,18 +985,24 @@ def test_clear_time_column(self):
         )
         client.clear_time_column(dataset_name="name")
         client.auto_ml_client.update_table_spec.assert_called_with(
-            table_spec={"name": "table", "time_column_spec_id": None}
+            request=automl_v1beta1.UpdateTableSpecRequest(
+                table_spec={"name": "table", "time_column_spec_id": None}
+            )
         )
 
     def test_get_model_evaluation(self):
         client = self.tables_client({}, {})
         client.get_model_evaluation(model_evaluation_name="x")
-        client.auto_ml_client.get_model_evaluation.assert_called_with(name="x")
+        client.auto_ml_client.get_model_evaluation.assert_called_with(
+            request=automl_v1beta1.GetModelEvaluationRequest(name="x")
+        )
 
     def test_list_model_evaluations_empty(self):
         client = self.tables_client({"list_model_evaluations.return_value": []}, {})
         ds = client.list_model_evaluations(model_name="model")
-        client.auto_ml_client.list_model_evaluations.assert_called_with(parent="model")
+        client.auto_ml_client.list_model_evaluations.assert_called_with(
+            request=automl_v1beta1.ListModelEvaluationsRequest(parent="model")
+        )
         assert ds == []
 
     def test_list_model_evaluations_not_empty(self):
@@ -840,7 +1015,9 @@ def test_list_model_evaluations_not_empty(self):
             {},
         )
         ds = client.list_model_evaluations(model_name="model")
-        client.auto_ml_client.list_model_evaluations.assert_called_with(parent="model")
+        client.auto_ml_client.list_model_evaluations.assert_called_with(
+            request=automl_v1beta1.ListModelEvaluationsRequest(parent="model")
+        )
         assert len(ds) == 1
         assert ds[0] == "eval"
 
@@ -853,8 +1030,10 @@ def test_list_models_empty(self):
             {},
         )
         ds = client.list_models()
-        client.auto_ml_client.location_path.assert_called_with(PROJECT, REGION)
-        client.auto_ml_client.list_models.assert_called_with(parent=LOCATION_PATH)
+
+        client.auto_ml_client.list_models.assert_called_with(
+            request=automl_v1beta1.ListModelsRequest(parent=LOCATION_PATH)
+        )
         assert ds == []
 
     def test_list_models_not_empty(self):
@@ -867,8 +1046,10 @@ def test_list_models_not_empty(self):
             {},
         )
         ds = client.list_models()
-        client.auto_ml_client.location_path.assert_called_with(PROJECT, REGION)
-        client.auto_ml_client.list_models.assert_called_with(parent=LOCATION_PATH)
+
+        client.auto_ml_client.list_models.assert_called_with(
+            request=automl_v1beta1.ListModelsRequest(parent=LOCATION_PATH)
+        )
         assert len(ds) == 1
         assert ds[0] == "some_model"
 
@@ -931,7 +1112,9 @@ def test_delete_model(self):
         model.configure_mock(name="name")
         client = self.tables_client({"delete_model.return_value": None}, {})
         client.delete_model(model=model)
-        client.auto_ml_client.delete_model.assert_called_with(name="name")
+        client.auto_ml_client.delete_model.assert_called_with(
+            request=automl_v1beta1.DeleteModelRequest(name="name")
+        )
 
     def test_delete_model_not_found(self):
         client = self.tables_client({"list_models.return_value": []}, {})
@@ -941,7 +1124,9 @@ def test_delete_model_not_found(self):
     def test_delete_model_name(self):
         client = self.tables_client({"delete_model.return_value": None}, {})
         client.delete_model(model_name="name")
-        client.auto_ml_client.delete_model.assert_called_with(name="name")
+        client.auto_ml_client.delete_model.assert_called_with(
+            request=automl_v1beta1.DeleteModelRequest(name="name")
+        )
 
     def test_deploy_model_no_args(self):
         client = self.tables_client({}, {})
@@ -952,7 +1137,9 @@ def test_deploy_model_no_args(self):
     def test_deploy_model(self):
         client = self.tables_client({}, {})
         client.deploy_model(model_name="name")
-        client.auto_ml_client.deploy_model.assert_called_with(name="name")
+        client.auto_ml_client.deploy_model.assert_called_with(
+            request=automl_v1beta1.DeployModelRequest(name="name")
+        )
 
     def test_deploy_model_not_found(self):
         client = self.tables_client({"list_models.return_value": []}, {})
@@ -963,7 +1150,9 @@ def test_deploy_model_not_found(self):
     def test_undeploy_model(self):
         client = self.tables_client({}, {})
         client.undeploy_model(model_name="name")
-        client.auto_ml_client.undeploy_model.assert_called_with(name="name")
+        client.auto_ml_client.undeploy_model.assert_called_with(
+            request=automl_v1beta1.UndeployModelRequest(name="name")
+        )
 
     def test_undeploy_model_not_found(self):
         client = self.tables_client({"list_models.return_value": []}, {})
@@ -989,32 +1178,37 @@ def test_create_model(self):
             "my_model", dataset_name="my_dataset", train_budget_milli_node_hours=1000
         )
         client.auto_ml_client.create_model.assert_called_with(
-            parent=LOCATION_PATH,
-            model={
-                "display_name": "my_model",
-                "dataset_id": "my_dataset",
-                "tables_model_metadata": {"train_budget_milli_node_hours": 1000},
-            },
+            request=automl_v1beta1.CreateModelRequest(
+                parent=LOCATION_PATH,
+                model={
+                    "display_name": "my_model",
+                    "dataset_id": "my_dataset",
+                    "tables_model_metadata": {"train_budget_milli_node_hours": 1000},
+                },
+            )
         )
 
     def test_create_model_include_columns(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock1 = mock.Mock()
-        column_spec_mock1.configure_mock(name="column/1", display_name="column1")
-        column_spec_mock2 = mock.Mock()
-        column_spec_mock2.configure_mock(name="column/2", display_name="column2")
+
+        column_spec_1 = automl_v1beta1.ColumnSpec(
+            name="column/1", display_name="column1"
+        )
+        column_spec_2 = automl_v1beta1.ColumnSpec(
+            name="column/2", display_name="column2"
+        )
+
         client = self.tables_client(
-            {
-                "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [
-                    column_spec_mock1,
-                    column_spec_mock2,
+            client_attrs={
+                "list_table_specs.return_value": [
+                    automl_v1beta1.TableSpec(name="table")
                 ],
+                "list_column_specs.return_value": [column_spec_1, column_spec_2],
                 "location_path.return_value": LOCATION_PATH,
             },
-            {},
+            prediction_client_attrs={},
         )
         client.create_model(
             "my_model",
@@ -1023,35 +1217,37 @@ def test_create_model_include_columns(self):
             train_budget_milli_node_hours=1000,
         )
         client.auto_ml_client.create_model.assert_called_with(
-            parent=LOCATION_PATH,
-            model={
-                "display_name": "my_model",
-                "dataset_id": "my_dataset",
-                "tables_model_metadata": {
-                    "train_budget_milli_node_hours": 1000,
-                    "input_feature_column_specs": [column_spec_mock1],
-                },
-            },
+            request=automl_v1beta1.CreateModelRequest(
+                parent=LOCATION_PATH,
+                model=automl_v1beta1.Model(
+                    display_name="my_model",
+                    dataset_id="my_dataset",
+                    tables_model_metadata=automl_v1beta1.TablesModelMetadata(
+                        train_budget_milli_node_hours=1000,
+                        input_feature_column_specs=[column_spec_1],
+                    ),
+                ),
+            )
         )
 
     def test_create_model_exclude_columns(self):
         table_spec_mock = mock.Mock()
         # name is reserved in use of __init__, needs to be passed here
         table_spec_mock.configure_mock(name="table")
-        column_spec_mock1 = mock.Mock()
-        column_spec_mock1.configure_mock(name="column/1", display_name="column1")
-        column_spec_mock2 = mock.Mock()
-        column_spec_mock2.configure_mock(name="column/2", display_name="column2")
+
+        column_spec_1 = automl_v1beta1.ColumnSpec(
+            name="column/1", display_name="column1"
+        )
+        column_spec_2 = automl_v1beta1.ColumnSpec(
+            name="column/2", display_name="column2"
+        )
         client = self.tables_client(
-            {
+            client_attrs={
                 "list_table_specs.return_value": [table_spec_mock],
-                "list_column_specs.return_value": [
-                    column_spec_mock1,
-                    column_spec_mock2,
-                ],
+                "list_column_specs.return_value": [column_spec_1, column_spec_2],
                 "location_path.return_value": LOCATION_PATH,
             },
-            {},
+            prediction_client_attrs={},
         )
         client.create_model(
             "my_model",
@@ -1060,15 +1256,17 @@ def test_create_model_exclude_columns(self):
             train_budget_milli_node_hours=1000,
         )
         client.auto_ml_client.create_model.assert_called_with(
-            parent=LOCATION_PATH,
-            model={
-                "display_name": "my_model",
-                "dataset_id": "my_dataset",
-                "tables_model_metadata": {
-                    "train_budget_milli_node_hours": 1000,
-                    "input_feature_column_specs": [column_spec_mock2],
-                },
-            },
+            request=automl_v1beta1.CreateModelRequest(
+                parent=LOCATION_PATH,
+                model=automl_v1beta1.Model(
+                    display_name="my_model",
+                    dataset_id="my_dataset",
+                    tables_model_metadata=automl_v1beta1.TablesModelMetadata(
+                        train_budget_milli_node_hours=1000,
+                        input_feature_column_specs=[column_spec_2],
+                    ),
+                ),
+            )
         )
 
     def test_create_model_invalid_hours_small(self):
@@ -1125,7 +1323,9 @@ def test_predict_from_array(self):
         payload = data_items.ExamplePayload(row=row)
 
         client.prediction_client.predict.assert_called_with(
-            name="my_model", payload=payload, params=None
+            request=automl_v1beta1.PredictRequest(
+                name="my_model", payload=payload, params=None
+            )
         )
 
     def test_predict_from_dict(self):
@@ -1149,7 +1349,9 @@ def test_predict_from_dict(self):
         payload = data_items.ExamplePayload(row=row)
 
         client.prediction_client.predict.assert_called_with(
-            name="my_model", payload=payload, params=None
+            request=automl_v1beta1.PredictRequest(
+                name="my_model", payload=payload, params=None
+            )
         )
 
     def test_predict_from_dict_with_feature_importance(self):
@@ -1175,7 +1377,9 @@ def test_predict_from_dict_with_feature_importance(self):
         payload = data_items.ExamplePayload(row=row)
 
         client.prediction_client.predict.assert_called_with(
-            name="my_model", payload=payload, params={"feature_importance": "true"}
+            request=automl_v1beta1.PredictRequest(
+                name="my_model", payload=payload, params={"feature_importance": "true"}
+            )
         )
 
     def test_predict_from_dict_missing(self):
@@ -1199,7 +1403,9 @@ def test_predict_from_dict_missing(self):
         payload = data_items.ExamplePayload(row=row)
 
         client.prediction_client.predict.assert_called_with(
-            name="my_model", payload=payload, params=None
+            request=automl_v1beta1.PredictRequest(
+                name="my_model", payload=payload, params=None
+            )
         )
 
     def test_predict_all_types(self):
@@ -1282,7 +1488,9 @@ def test_predict_all_types(self):
         payload = data_items.ExamplePayload(row=row)
 
         client.prediction_client.predict.assert_called_with(
-            name="my_model", payload=payload, params=None
+            request=automl_v1beta1.PredictRequest(
+                name="my_model", payload=payload, params=None
+            )
         )
 
     def test_predict_from_array_missing(self):
@@ -1316,9 +1524,11 @@ def test_batch_predict_pandas_dataframe(self):
         client.gcs_client.upload_pandas_dataframe.assert_called_with(dataframe)
 
         client.prediction_client.batch_predict.assert_called_with(
-            name="my_model",
-            input_config={"gcs_source": {"input_uris": ["gs://input"]}},
-            output_config={"gcs_destination": {"output_uri_prefix": "gs://output"}},
+            request=automl_v1beta1.BatchPredictRequest(
+                name="my_model",
+                input_config={"gcs_source": {"input_uris": ["gs://input"]}},
+                output_config={"gcs_destination": {"output_uri_prefix": "gs://output"}},
+            )
         )
 
     def test_batch_predict_pandas_dataframe_init_gcs(self):
@@ -1350,9 +1560,13 @@ def test_batch_predict_pandas_dataframe_init_gcs(self):
             client.gcs_client.upload_pandas_dataframe.assert_called_with(dataframe)
 
             client.prediction_client.batch_predict.assert_called_with(
-                name="my_model",
-                input_config={"gcs_source": {"input_uris": ["gs://input"]}},
-                output_config={"gcs_destination": {"output_uri_prefix": "gs://output"}},
+                request=automl_v1beta1.BatchPredictRequest(
+                    name="my_model",
+                    input_config={"gcs_source": {"input_uris": ["gs://input"]}},
+                    output_config={
+                        "gcs_destination": {"output_uri_prefix": "gs://output"}
+                    },
+                )
             )
 
     def test_batch_predict_gcs(self):
@@ -1363,9 +1577,11 @@ def test_batch_predict_gcs(self):
             gcs_output_uri_prefix="gs://output",
         )
         client.prediction_client.batch_predict.assert_called_with(
-            name="my_model",
-            input_config={"gcs_source": {"input_uris": ["gs://input"]}},
-            output_config={"gcs_destination": {"output_uri_prefix": "gs://output"}},
+            request=automl_v1beta1.BatchPredictRequest(
+                name="my_model",
+                input_config={"gcs_source": {"input_uris": ["gs://input"]}},
+                output_config={"gcs_destination": {"output_uri_prefix": "gs://output"}},
+            )
         )
 
     def test_batch_predict_bigquery(self):
@@ -1376,9 +1592,11 @@ def test_batch_predict_bigquery(self):
             bigquery_output_uri="bq://output",
         )
         client.prediction_client.batch_predict.assert_called_with(
-            name="my_model",
-            input_config={"bigquery_source": {"input_uri": "bq://input"}},
-            output_config={"bigquery_destination": {"output_uri": "bq://output"}},
+            request=automl_v1beta1.BatchPredictRequest(
+                name="my_model",
+                input_config={"bigquery_source": {"input_uri": "bq://input"}},
+                output_config={"bigquery_destination": {"output_uri": "bq://output"}},
+            )
         )
 
     def test_batch_predict_mixed(self):
@@ -1389,9 +1607,11 @@ def test_batch_predict_mixed(self):
             bigquery_output_uri="bq://output",
         )
         client.prediction_client.batch_predict.assert_called_with(
-            name="my_model",
-            input_config={"gcs_source": {"input_uris": ["gs://input"]}},
-            output_config={"bigquery_destination": {"output_uri": "bq://output"}},
+            request=automl_v1beta1.BatchPredictRequest(
+                name="my_model",
+                input_config={"gcs_source": {"input_uris": ["gs://input"]}},
+                output_config={"bigquery_destination": {"output_uri": "bq://output"}},
+            )
         )
 
     def test_batch_predict_missing_input_gcs_uri(self):