diff --git a/automl/beta/automl_vision_create_model_test.py b/automl/beta/automl_vision_create_model_test.py
deleted file mode 100644
index bcb6c323e1e4..000000000000
--- a/automl/beta/automl_vision_create_model_test.py
+++ /dev/null
@@ -1,43 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import datetime
-import os
-
-from google.cloud import automl_v1beta1 as automl
-import pytest
-
-project_id = os.environ["GOOGLE_CLOUD_PROJECT"]
-compute_region = "us-central1"
-
-
-@pytest.mark.skip(reason="creates too many models")
-def test_model_create_status_delete(capsys):
-    # create model
-    client = automl.AutoMlClient()
-    model_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    project_location = client.location_path(project_id, compute_region)
-    my_model = {
-        "display_name": model_name,
-        "dataset_id": "3946265060617537378",
-        "image_classification_model_metadata": {"train_budget": 24},
-    }
-    response = client.create_model(project_location, my_model)
-    operation_name = response.operation.name
-    assert operation_name
-
-    # cancel operation
-    response.cancel()
diff --git a/automl/beta/automl_vision_model.py b/automl/beta/automl_vision_model.py
deleted file mode 100755
index 04aa4c9476e4..000000000000
--- a/automl/beta/automl_vision_model.py
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""This application demonstrates how to perform basic operations on model
-with the Google AutoML Vision API.
-
-For more information, the documentation at
-https://cloud.google.com/vision/automl/docs.
-"""
-
-import argparse
-import os
-
-
-def create_model(
-    project_id, compute_region, dataset_id, model_name, train_budget=24
-):
-    """Create a model."""
-    # [START automl_vision_create_model]
-    # TODO(developer): Uncomment and set the following variables
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # dataset_id = 'DATASET_ID_HERE'
-    # model_name = 'MODEL_NAME_HERE'
-    # train_budget = integer amount for maximum cost of model
-
-    from google.cloud import automl_v1beta1 as automl
-
-    client = automl.AutoMlClient()
-
-    # A resource that represents Google Cloud Platform location.
-    project_location = client.location_path(project_id, compute_region)
-
-    # Set model name and model metadata for the image dataset.
-    my_model = {
-        "display_name": model_name,
-        "dataset_id": dataset_id,
-        "image_classification_model_metadata": {"train_budget": train_budget}
-        if train_budget
-        else {},
-    }
-
-    # Create a model with the model metadata in the region.
-    response = client.create_model(project_location, my_model)
-
-    print("Training operation name: {}".format(response.operation.name))
-    print("Training started...")
-
-    # [END automl_vision_create_model]
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    subparsers = parser.add_subparsers(dest="command")
-
-    create_model_parser = subparsers.add_parser(
-        "create_model", help=create_model.__doc__
-    )
-    create_model_parser.add_argument("dataset_id")
-    create_model_parser.add_argument("model_name")
-    create_model_parser.add_argument(
-        "train_budget", type=int, nargs="?", default=0
-    )
-
-    project_id = os.environ["PROJECT_ID"]
-    compute_region = os.environ["REGION_NAME"]
-
-    args = parser.parse_args()
-
-    if args.command == "create_model":
-        create_model(
-            project_id,
-            compute_region,
-            args.dataset_id,
-            args.model_name,
-            args.train_budget,
-        )
diff --git a/automl/beta/requirements.txt b/automl/beta/requirements.txt
index d2157cd180ba..94f5d6c85200 100644
--- a/automl/beta/requirements.txt
+++ b/automl/beta/requirements.txt
@@ -1 +1 @@
-google-cloud-automl==2.0.0
+google-cloud-automl==2.1.0
diff --git a/automl/snippets/automl_translation_dataset.py b/automl/snippets/automl_translation_dataset.py
index e1dd739ad935..674c2c7c485a 100755
--- a/automl/snippets/automl_translation_dataset.py
+++ b/automl/snippets/automl_translation_dataset.py
@@ -25,138 +25,6 @@
 import os
 
 
-def create_dataset(project_id, compute_region, dataset_name, source, target):
-    """Create a dataset."""
-    # [START automl_translate_create_dataset]
-    # TODO(developer): Uncomment and set the following variables
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # dataset_name = 'DATASET_NAME_HERE'
-    # source = 'LANGUAGE_CODE_OF_SOURCE_LANGUAGE'
-    # target = 'LANGUAGE_CODE_OF_TARGET_LANGUAGE'
-
-    from google.cloud import automl_v1beta1 as automl
-
-    client = automl.AutoMlClient()
-
-    # A resource that represents Google Cloud Platform location.
-    project_location = f"projects/{project_id}/locations/{compute_region}"
-
-    # Specify the source and target language.
-    dataset_metadata = {
-        "source_language_code": source,
-        "target_language_code": target,
-    }
-    # Set dataset name and dataset metadata
-    my_dataset = {
-        "display_name": dataset_name,
-        "translation_dataset_metadata": dataset_metadata,
-    }
-
-    # Create a dataset with the dataset metadata in the region.
-    dataset = client.create_dataset(parent=project_location, dataset=my_dataset)
-
-    # Display the dataset information
-    print("Dataset name: {}".format(dataset.name))
-    print("Dataset id: {}".format(dataset.name.split("/")[-1]))
-    print("Dataset display name: {}".format(dataset.display_name))
-    print("Translation dataset Metadata:")
-    print(
-        "\tsource_language_code: {}".format(
-            dataset.translation_dataset_metadata.source_language_code
-        )
-    )
-    print(
-        "\ttarget_language_code: {}".format(
-            dataset.translation_dataset_metadata.target_language_code
-        )
-    )
-    print("Dataset create time: {}".format(dataset.create_time))
-
-    # [END automl_translate_create_dataset]
-
-
-def list_datasets(project_id, compute_region, filter_):
-    """List Datasets."""
-    # [START automl_translate_list_datasets]
-    # TODO(developer): Uncomment and set the following variables
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # filter_ = 'filter expression here'
-
-    from google.cloud import automl_v1beta1 as automl
-
-    client = automl.AutoMlClient()
-
-    # A resource that represents Google Cloud Platform location.
-    project_location = f"projects/{project_id}/locations/{compute_region}"
-
-    # List all the datasets available in the region by applying filter.
-    request = automl.ListDatasetsRequest(parent=project_location, filter=filter_)
-    response = client.list_datasets(request=request)
-
-    print("List of datasets:")
-    for dataset in response:
-        # Display the dataset information
-        print("Dataset name: {}".format(dataset.name))
-        print("Dataset id: {}".format(dataset.name.split("/")[-1]))
-        print("Dataset display name: {}".format(dataset.display_name))
-        print("Translation dataset metadata:")
-        print(
-            "\tsource_language_code: {}".format(
-                dataset.translation_dataset_metadata.source_language_code
-            )
-        )
-        print(
-            "\ttarget_language_code: {}".format(
-                dataset.translation_dataset_metadata.target_language_code
-            )
-        )
-        print("Dataset create time: {}".format(dataset.create_time))
-
-    # [END automl_translate_list_datasets]
-
-
-def get_dataset(project_id, compute_region, dataset_id):
-    """Get the dataset."""
-    # [START automl_translate_get_dataset]
-    # TODO(developer): Uncomment and set the following variables
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # dataset_id = 'DATASET_ID_HERE'
-
-    from google.cloud import automl_v1beta1 as automl
-
-    client = automl.AutoMlClient()
-
-    # Get the full path of the dataset
-    dataset_full_id = client.dataset_path(
-        project_id, compute_region, dataset_id
-    )
-
-    # Get complete detail of the dataset.
-    dataset = client.get_dataset(name=dataset_full_id)
-
-    # Display the dataset information
-    print("Dataset name: {}".format(dataset.name))
-    print("Dataset id: {}".format(dataset.name.split("/")[-1]))
-    print("Dataset display name: {}".format(dataset.display_name))
-    print("Translation dataset metadata:")
-    print(
-        "\tsource_language_code: {}".format(
-            dataset.translation_dataset_metadata.source_language_code
-        )
-    )
-    print(
-        "\ttarget_language_code: {}".format(
-            dataset.translation_dataset_metadata.target_language_code
-        )
-    )
-    print("Dataset create time: {}".format(dataset.create_time))
-
-    # [END automl_translate_get_dataset]
-
-
 def import_data(project_id, compute_region, dataset_id, path):
     """Import sentence pairs to the dataset."""
     # [START automl_translate_import_data]
@@ -171,9 +39,7 @@ def import_data(project_id, compute_region, dataset_id, path):
     client = automl.AutoMlClient()
 
     # Get the full path of the dataset.
-    dataset_full_id = client.dataset_path(
-        project_id, compute_region, dataset_id
-    )
+    dataset_full_id = client.dataset_path(project_id, compute_region, dataset_id)
 
     # Get the multiple Google Cloud Storage URIs
     input_uris = path.split(",")
@@ -202,9 +68,7 @@ def delete_dataset(project_id, compute_region, dataset_id):
     client = automl.AutoMlClient()
 
     # Get the full path of the dataset.
-    dataset_full_id = client.dataset_path(
-        project_id, compute_region, dataset_id
-    )
+    dataset_full_id = client.dataset_path(project_id, compute_region, dataset_id)
 
     # Delete a dataset.
     response = client.delete_dataset(name=dataset_full_id)
@@ -222,21 +86,7 @@ def delete_dataset(project_id, compute_region, dataset_id):
     )
     subparsers = parser.add_subparsers(dest="command")
 
-    create_dataset_parser = subparsers.add_parser(
-        "create_dataset", help=create_dataset.__doc__
-    )
-    create_dataset_parser.add_argument("dataset_name")
-    create_dataset_parser.add_argument("source")
-    create_dataset_parser.add_argument("target")
-
-    list_datasets_parser = subparsers.add_parser(
-        "list_datasets", help=list_datasets.__doc__
-    )
-    list_datasets_parser.add_argument("filter", nargs="?", default="")
-
-    import_data_parser = subparsers.add_parser(
-        "import_data", help=import_data.__doc__
-    )
+    import_data_parser = subparsers.add_parser("import_data", help=import_data.__doc__)
     import_data_parser.add_argument("dataset_id")
     import_data_parser.add_argument("path")
 
@@ -245,28 +95,11 @@ def delete_dataset(project_id, compute_region, dataset_id):
     )
     delete_dataset_parser.add_argument("dataset_id")
 
-    get_dataset_parser = subparsers.add_parser(
-        "get_dataset", help=get_dataset.__doc__
-    )
-    get_dataset_parser.add_argument("dataset_id")
-
     project_id = os.environ["PROJECT_ID"]
     compute_region = os.environ["REGION_NAME"]
 
     args = parser.parse_args()
 
-    if args.command == "create_dataset":
-        create_dataset(
-            project_id,
-            compute_region,
-            args.dataset_name,
-            args.source,
-            args.target,
-        )
-    if args.command == "list_datasets":
-        list_datasets(project_id, compute_region, args.filter)
-    if args.command == "get_dataset":
-        get_dataset(project_id, compute_region, args.dataset_id)
     if args.command == "import_data":
         import_data(project_id, compute_region, args.dataset_id, args.path)
     if args.command == "delete_dataset":
diff --git a/automl/snippets/automl_translation_model.py b/automl/snippets/automl_translation_model.py
index 4f9d165580fd..1b1e45c91a52 100755
--- a/automl/snippets/automl_translation_model.py
+++ b/automl/snippets/automl_translation_model.py
@@ -25,38 +25,6 @@
 import os
 
 
-def create_model(project_id, compute_region, dataset_id, model_name):
-    """Create a model."""
-    # [START automl_translate_create_model]
-    # TODO(developer): Uncomment and set the following variables
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # dataset_id = 'DATASET_ID_HERE'
-    # model_name = 'MODEL_NAME_HERE'
-
-    from google.cloud import automl_v1beta1 as automl
-
-    client = automl.AutoMlClient()
-
-    # A resource that represents Google Cloud Platform location.
-    project_location = client.location_path(project_id, compute_region)
-
-    # Set model name and dataset.
-    my_model = {
-        "display_name": model_name,
-        "dataset_id": dataset_id,
-        "translation_model_metadata": {"base_model": ""},
-    }
-
-    # Create a model with the model metadata in the region.
-    response = client.create_model(parent=project_location, model=my_model)
-
-    print("Training operation name: {}".format(response.operation.name))
-    print("Training started...")
-
-    # [END automl_translate_create_model]
-
-
 def list_models(project_id, compute_region, filter_):
     """List all models."""
     # [START automl_translate_list_models]
@@ -127,60 +95,6 @@ def get_model(project_id, compute_region, model_id):
     # [END automl_translate_get_model]
 
 
-def list_model_evaluations(project_id, compute_region, model_id, filter_):
-    """List model evaluations."""
-    # [START automl_translate_list_model_evaluations]
-    # TODO(developer): Uncomment and set the following variables
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # model_id = 'MODEL_ID_HERE'
-    # filter_ = 'filter expression here'
-
-    from google.cloud import automl_v1beta1 as automl
-
-    client = automl.AutoMlClient()
-
-    # Get the full path of the model.
-    model_full_id = client.model_path(project_id, compute_region, model_id)
-
-    print("List of model evaluations:")
-    request = automl.ListModelEvaluationsRequest(
-        parent=model_full_id,
-        filter=filter_
-    )
-    for element in client.list_model_evaluations(request=request):
-        print(element)
-
-    # [END automl_translate_list_model_evaluations]
-
-
-def get_model_evaluation(
-    project_id, compute_region, model_id, model_evaluation_id
-):
-    """Get model evaluation."""
-    # [START automl_translate_get_model_evaluation]
-    # TODO(developer): Uncomment and set the following variables
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # model_id = 'MODEL_ID_HERE'
-    # model_evaluation_id = 'MODEL_EVALUATION_ID_HERE'
-
-    from google.cloud import automl_v1beta1 as automl
-
-    client = automl.AutoMlClient()
-
-    # Get the full path of the model evaluation.
-    model_path = client.model_path(project_id, compute_region, model_id)
-    model_evaluation_full_id = f"{model_path}/modelEvaluations/{model_evaluation_id}"
-
-    # Get complete detail of the model evaluation.
-    response = client.get_model_evaluation(name=model_evaluation_full_id)
-
-    print(response)
-
-    # [END automl_translate_get_model_evaluation]
-
-
 def delete_model(project_id, compute_region, model_id):
     """Delete a model."""
     # [START automl_translate_delete_model]
@@ -217,9 +131,7 @@ def get_operation_status(operation_full_id):
     client = automl.AutoMlClient()
 
     # Get the latest state of a long-running operation.
-    response = client._transport.operations_client.get_operation(
-        operation_full_id
-    )
+    response = client._transport.operations_client.get_operation(operation_full_id)
 
     print("Operation status: {}".format(response))
 
@@ -233,27 +145,7 @@ def get_operation_status(operation_full_id):
     )
     subparsers = parser.add_subparsers(dest="command")
 
-    create_model_parser = subparsers.add_parser(
-        "create_model", help=create_model.__doc__
-    )
-    create_model_parser.add_argument("dataset_id")
-    create_model_parser.add_argument("model_name")
-
-    list_model_evaluations_parser = subparsers.add_parser(
-        "list_model_evaluations", help=list_model_evaluations.__doc__
-    )
-    list_model_evaluations_parser.add_argument("model_id")
-    list_model_evaluations_parser.add_argument("filter", nargs="?", default="")
-
-    get_model_evaluation_parser = subparsers.add_parser(
-        "get_model_evaluation", help=get_model_evaluation.__doc__
-    )
-    get_model_evaluation_parser.add_argument("model_id")
-    get_model_evaluation_parser.add_argument("model_evaluation_id")
-
-    get_model_parser = subparsers.add_parser(
-        "get_model", help=get_model.__doc__
-    )
+    get_model_parser = subparsers.add_parser("get_model", help=get_model.__doc__)
     get_model_parser.add_argument("model_id")
 
     get_operation_status_parser = subparsers.add_parser(
@@ -261,9 +153,7 @@ def get_operation_status(operation_full_id):
     )
     get_operation_status_parser.add_argument("operation_full_id")
 
-    list_models_parser = subparsers.add_parser(
-        "list_models", help=list_models.__doc__
-    )
+    list_models_parser = subparsers.add_parser("list_models", help=list_models.__doc__)
     list_models_parser.add_argument("filter", nargs="?", default="")
 
     delete_model_parser = subparsers.add_parser(
@@ -276,22 +166,10 @@ def get_operation_status(operation_full_id):
 
     args = parser.parse_args()
 
-    if args.command == "create_model":
-        create_model(
-            project_id, compute_region, args.dataset_id, args.model_name
-        )
     if args.command == "list_models":
         list_models(project_id, compute_region, args.filter)
     if args.command == "get_model":
         get_model(project_id, compute_region, args.model_id)
-    if args.command == "list_model_evaluations":
-        list_model_evaluations(
-            project_id, compute_region, args.model_id, args.filter
-        )
-    if args.command == "get_model_evaluation":
-        get_model_evaluation(
-            project_id, compute_region, args.model_id, args.model_evaluation_id
-        )
     if args.command == "delete_model":
         delete_model(project_id, compute_region, args.model_id)
     if args.command == "get_operation_status":
diff --git a/automl/snippets/automl_translation_predict.py b/automl/snippets/automl_translation_predict.py
deleted file mode 100644
index 70c14e3634cf..000000000000
--- a/automl/snippets/automl_translation_predict.py
+++ /dev/null
@@ -1,90 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""This application demonstrates how to perform basic operations on prediction
-with the Google AutoML Translation API.
-
-For more information, see the documentation at
-https://cloud.google.com/translate/automl/docs
-"""
-
-import argparse
-import os
-
-
-def predict(project_id, compute_region, model_id, file_path):
-    """Translate the content."""
-    # [START automl_translate_predict]
-    # project_id = 'PROJECT_ID_HERE'
-    # compute_region = 'COMPUTE_REGION_HERE'
-    # model_id = 'MODEL_ID_HERE'
-    # file_path = '/local/path/to/file'
-
-    from google.cloud import automl_v1beta1 as automl
-
-    automl_client = automl.AutoMlClient()
-
-    # Create client for prediction service.
-    prediction_client = automl.PredictionServiceClient()
-
-    # Get the full path of the model.
-    model_full_id = automl_client.model_path(
-        project_id, compute_region, model_id
-    )
-
-    # Read the file content for translation.
-    with open(file_path, "rb") as content_file:
-        content = content_file.read()
-    content.decode("utf-8")
-
-    # Set the payload by giving the content of the file.
-    payload = {"text_snippet": {"content": content}}
-
-    # params is additional domain-specific parameters.
-    params = {}
-
-    request = automl.PredictRequest(
-        name=model_full_id,
-        payload=payload,
-        params=params
-    )
-
-    response = prediction_client.predict(request=request)
-    translated_content = response.payload[0].translation.translated_content
-
-    print(u"Translated content: {}".format(translated_content.content))
-
-    # [END automl_translate_predict]
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description=__doc__,
-        formatter_class=argparse.RawDescriptionHelpFormatter,
-    )
-    subparsers = parser.add_subparsers(dest="command")
-
-    predict_parser = subparsers.add_parser("predict", help=predict.__doc__)
-    predict_parser.add_argument("model_id")
-    predict_parser.add_argument("file_path")
-
-    project_id = os.environ["PROJECT_ID"]
-    compute_region = os.environ["REGION_NAME"]
-
-    args = parser.parse_args()
-
-    if args.command == "predict":
-        predict(project_id, compute_region, args.model_id, args.file_path)
diff --git a/automl/snippets/batch_predict.py b/automl/snippets/batch_predict.py
index 427fadf22b27..5b5f810b4ed7 100644
--- a/automl/snippets/batch_predict.py
+++ b/automl/snippets/batch_predict.py
@@ -33,14 +33,10 @@ def batch_predict(project_id, model_id, input_uri, output_uri):
 
     input_config = automl.BatchPredictInputConfig(gcs_source=gcs_source)
     gcs_destination = automl.GcsDestination(output_uri_prefix=output_uri)
-    output_config = automl.BatchPredictOutputConfig(
-        gcs_destination=gcs_destination
-    )
+    output_config = automl.BatchPredictOutputConfig(gcs_destination=gcs_destination)
 
     response = prediction_client.batch_predict(
-        name=model_full_id,
-        input_config=input_config,
-        output_config=output_config
+        name=model_full_id, input_config=input_config, output_config=output_config
     )
 
     print("Waiting for operation to complete...")
diff --git a/automl/snippets/batch_predict_test.py b/automl/snippets/batch_predict_test.py
index 2869873a9198..483ea5187ccc 100644
--- a/automl/snippets/batch_predict_test.py
+++ b/automl/snippets/batch_predict_test.py
@@ -20,9 +20,7 @@
 PROJECT_ID = os.environ["AUTOML_PROJECT_ID"]
 BUCKET_ID = "{}-lcm".format(PROJECT_ID)
 MODEL_ID = "TEN0000000000000000000"
-PREFIX = "TEST_EXPORT_OUTPUT_" + datetime.datetime.now().strftime(
-    "%Y%m%d%H%M%S"
-)
+PREFIX = "TEST_EXPORT_OUTPUT_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
 
 
 def test_batch_predict(capsys):
@@ -32,16 +30,8 @@ def test_batch_predict(capsys):
     try:
         input_uri = "gs://{}/entity-extraction/input.jsonl".format(BUCKET_ID)
         output_uri = "gs://{}/{}/".format(BUCKET_ID, PREFIX)
-        batch_predict.batch_predict(
-            PROJECT_ID, MODEL_ID, input_uri, output_uri
-        )
+        batch_predict.batch_predict(PROJECT_ID, MODEL_ID, input_uri, output_uri)
         out, _ = capsys.readouterr()
-        assert (
-            "does not exist"
-            in out
-        )
+        assert "does not exist" in out
     except Exception as e:
-        assert (
-            "does not exist"
-            in e.message
-        )
+        assert "does not exist" in e.message
diff --git a/automl/snippets/dataset_test.py b/automl/snippets/dataset_test.py
index 6ae5326c026b..a2512d57e40a 100644
--- a/automl/snippets/dataset_test.py
+++ b/automl/snippets/dataset_test.py
@@ -15,55 +15,31 @@
 # limitations under the License.
 
 import os
-import uuid
-
-import pytest
 
 import automl_translation_dataset
 
-project_id = os.environ["GOOGLE_CLOUD_PROJECT"]
-compute_region = "us-central1"
-dataset_id = "TRL3876092572857648864"
-
-
-@pytest.mark.slow
-def test_dataset_create_import_delete(capsys):
-    # create dataset
-    dataset_name = f"test_{uuid.uuid4().hex[:27]}"
-    automl_translation_dataset.create_dataset(
-        project_id, compute_region, dataset_name, "en", "ja"
-    )
-    out, _ = capsys.readouterr()
-    create_dataset_output = out.splitlines()
-    assert "Dataset id: " in create_dataset_output[1]
-
-    # import data
-    dataset_id = create_dataset_output[1].split()[2]
-    data = "gs://{}-vcm/en-ja.csv".format(project_id)
-    automl_translation_dataset.import_data(
-        project_id, compute_region, dataset_id, data
-    )
-    out, _ = capsys.readouterr()
-    assert "Data imported." in out
-
-    # delete dataset
-    automl_translation_dataset.delete_dataset(
-        project_id, compute_region, dataset_id
-    )
-    out, _ = capsys.readouterr()
-    assert "Dataset deleted." in out
-
-
-def test_dataset_list_get(capsys):
-    # list datasets
-    automl_translation_dataset.list_datasets(project_id, compute_region, "")
-    out, _ = capsys.readouterr()
-    list_dataset_output = out.splitlines()
-    assert "Dataset id: " in list_dataset_output[2]
-
-    # get dataset
-    automl_translation_dataset.get_dataset(
-        project_id, compute_region, dataset_id
-    )
-    out, _ = capsys.readouterr()
-    assert "Dataset name: " in out
+PROJECT_ID = os.environ["GOOGLE_CLOUD_PROJECT"]
+BUCKET_ID = "{}-lcm".format(PROJECT_ID)
+COMPUTE_REGION = "us-central1"
+DATASET_ID = "TRL00000000000000"
+
+
+def test_import_dataset(capsys):
+    # As importing a dataset can take a long time and only four operations can
+    # be run on a dataset at once. Try to import into a nonexistent dataset and
+    # confirm that the dataset was not found, but other elements of the request
+    # were valid.
+    try:
+        data = "gs://{}/sentiment-analysis/dataset.csv".format(BUCKET_ID)
+        automl_translation_dataset.import_data(
+            PROJECT_ID, COMPUTE_REGION, DATASET_ID, data
+        )
+        out, _ = capsys.readouterr()
+        assert (
+            "The Dataset doesn't exist or is inaccessible for use with AutoMl." in out
+        )
+    except Exception as e:
+        assert (
+            "The Dataset doesn't exist or is inaccessible for use with AutoMl."
+            in e.message
+        )
diff --git a/automl/snippets/delete_dataset.py b/automl/snippets/delete_dataset.py
index 23846b43ce1c..284ca704d9a2 100644
--- a/automl/snippets/delete_dataset.py
+++ b/automl/snippets/delete_dataset.py
@@ -24,9 +24,7 @@ def delete_dataset(project_id, dataset_id):
 
     client = automl.AutoMlClient()
     # Get the full path of the dataset
-    dataset_full_id = client.dataset_path(
-        project_id, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(project_id, "us-central1", dataset_id)
     response = client.delete_dataset(name=dataset_full_id)
 
     print("Dataset deleted. {}".format(response.result()))
diff --git a/automl/snippets/export_dataset.py b/automl/snippets/export_dataset.py
index 6be80907985d..bd68ca3116a6 100644
--- a/automl/snippets/export_dataset.py
+++ b/automl/snippets/export_dataset.py
@@ -26,9 +26,7 @@ def export_dataset(project_id, dataset_id, gcs_uri):
     client = automl.AutoMlClient()
 
     # Get the full path of the dataset
-    dataset_full_id = client.dataset_path(
-        project_id, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(project_id, "us-central1", dataset_id)
 
     gcs_destination = automl.GcsDestination(output_uri_prefix=gcs_uri)
     output_config = automl.OutputConfig(gcs_destination=gcs_destination)
diff --git a/automl/snippets/export_dataset_test.py b/automl/snippets/export_dataset_test.py
index de8bfe5aefa0..62b899b06c5c 100644
--- a/automl/snippets/export_dataset_test.py
+++ b/automl/snippets/export_dataset_test.py
@@ -19,9 +19,7 @@
 
 PROJECT_ID = os.environ["AUTOML_PROJECT_ID"]
 BUCKET_ID = "{}-lcm".format(PROJECT_ID)
-PREFIX = "TEST_EXPORT_OUTPUT_" + datetime.datetime.now().strftime(
-    "%Y%m%d%H%M%S"
-)
+PREFIX = "TEST_EXPORT_OUTPUT_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
 DATASET_ID = "TEN0000000000000000000"
 
 
@@ -36,8 +34,7 @@ def test_export_dataset(capsys):
         )
         out, _ = capsys.readouterr()
         assert (
-            "The Dataset doesn't exist or is inaccessible for use with AutoMl."
-            in out
+            "The Dataset doesn't exist or is inaccessible for use with AutoMl." in out
         )
     except Exception as e:
         assert (
diff --git a/automl/snippets/get_dataset.py b/automl/snippets/get_dataset.py
index b0ce2c8ae93d..6fa55a2ec965 100644
--- a/automl/snippets/get_dataset.py
+++ b/automl/snippets/get_dataset.py
@@ -29,9 +29,7 @@ def get_dataset(project_id, dataset_id):
 
     client = automl.AutoMlClient()
     # Get the full path of the dataset
-    dataset_full_id = client.dataset_path(
-        project_id, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(project_id, "us-central1", dataset_id)
     dataset = client.get_dataset(name=dataset_full_id)
 
     # Display the dataset information
diff --git a/automl/snippets/get_model_evaluation.py b/automl/snippets/get_model_evaluation.py
index 905410218062..1f526664bf36 100644
--- a/automl/snippets/get_model_evaluation.py
+++ b/automl/snippets/get_model_evaluation.py
@@ -39,9 +39,7 @@ def get_model_evaluation(project_id, model_id, model_evaluation_id):
     print("Model evaluation name: {}".format(response.name))
     print("Model annotation spec id: {}".format(response.annotation_spec_id))
     print("Create Time: {}".format(response.create_time))
-    print(
-        "Evaluation example count: {}".format(response.evaluated_example_count)
-    )
+    print("Evaluation example count: {}".format(response.evaluated_example_count))
     # [END automl_language_sentiment_analysis_get_model_evaluation]
     # [END automl_language_text_classification_get_model_evaluation]
     # [END automl_translate_get_model_evaluation]
diff --git a/automl/snippets/get_model_evaluation_test.py b/automl/snippets/get_model_evaluation_test.py
index 8b868cb362cb..7d694e55f728 100644
--- a/automl/snippets/get_model_evaluation_test.py
+++ b/automl/snippets/get_model_evaluation_test.py
@@ -38,8 +38,6 @@ def model_evaluation_id():
 
 
 def test_get_model_evaluation(capsys, model_evaluation_id):
-    get_model_evaluation.get_model_evaluation(
-        PROJECT_ID, MODEL_ID, model_evaluation_id
-    )
+    get_model_evaluation.get_model_evaluation(PROJECT_ID, MODEL_ID, model_evaluation_id)
     out, _ = capsys.readouterr()
     assert "Model evaluation name: " in out
diff --git a/automl/snippets/get_operation_status.py b/automl/snippets/get_operation_status.py
index d2ac0cc2819d..5d17dd5fc964 100644
--- a/automl/snippets/get_operation_status.py
+++ b/automl/snippets/get_operation_status.py
@@ -24,9 +24,7 @@ def get_operation_status(operation_full_id):
 
     client = automl.AutoMlClient()
     # Get the latest state of a long-running operation.
-    response = client._transport.operations_client.get_operation(
-        operation_full_id
-    )
+    response = client._transport.operations_client.get_operation(operation_full_id)
 
     print("Name: {}".format(response.name))
     print("Operation details:")
diff --git a/automl/snippets/import_dataset.py b/automl/snippets/import_dataset.py
index 3334fb3a4e9a..cdda1431feeb 100644
--- a/automl/snippets/import_dataset.py
+++ b/automl/snippets/import_dataset.py
@@ -25,9 +25,7 @@ def import_dataset(project_id, dataset_id, path):
 
     client = automl.AutoMlClient()
     # Get the full path of the dataset.
-    dataset_full_id = client.dataset_path(
-        project_id, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(project_id, "us-central1", dataset_id)
     # Get the multiple Google Cloud Storage URIs
     input_uris = path.split(",")
     gcs_source = automl.GcsSource(input_uris=input_uris)
diff --git a/automl/snippets/import_dataset_test.py b/automl/snippets/import_dataset_test.py
index 35d23edc7e8f..2b4f578f7d66 100644
--- a/automl/snippets/import_dataset_test.py
+++ b/automl/snippets/import_dataset_test.py
@@ -31,8 +31,7 @@ def test_import_dataset(capsys):
         import_dataset.import_dataset(PROJECT_ID, DATASET_ID, data)
         out, _ = capsys.readouterr()
         assert (
-            "The Dataset doesn't exist or is inaccessible for use with AutoMl."
-            in out
+            "The Dataset doesn't exist or is inaccessible for use with AutoMl." in out
         )
     except Exception as e:
         assert (
diff --git a/automl/snippets/language_entity_extraction_create_dataset_test.py b/automl/snippets/language_entity_extraction_create_dataset_test.py
index 13cd99115676..c4841cdda4fa 100644
--- a/automl/snippets/language_entity_extraction_create_dataset_test.py
+++ b/automl/snippets/language_entity_extraction_create_dataset_test.py
@@ -26,17 +26,13 @@
 def test_entity_extraction_create_dataset(capsys):
     # create dataset
     dataset_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    language_entity_extraction_create_dataset.create_dataset(
-        PROJECT_ID, dataset_name
-    )
+    language_entity_extraction_create_dataset.create_dataset(PROJECT_ID, dataset_name)
     out, _ = capsys.readouterr()
     assert "Dataset id: " in out
 
     # Delete the created dataset
     dataset_id = out.splitlines()[1].split()[2]
     client = automl.AutoMlClient()
-    dataset_full_id = client.dataset_path(
-        PROJECT_ID, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(PROJECT_ID, "us-central1", dataset_id)
     response = client.delete_dataset(name=dataset_full_id)
     response.result()
diff --git a/automl/snippets/language_entity_extraction_predict.py b/automl/snippets/language_entity_extraction_predict.py
index 8caea632a716..f6937d2ac331 100644
--- a/automl/snippets/language_entity_extraction_predict.py
+++ b/automl/snippets/language_entity_extraction_predict.py
@@ -26,28 +26,18 @@ def predict(project_id, model_id, content):
     prediction_client = automl.PredictionServiceClient()
 
     # Get the full path of the model.
-    model_full_id = automl.AutoMlClient.model_path(
-        project_id, "us-central1", model_id
-    )
+    model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
 
     # Supported mime_types: 'text/plain', 'text/html'
     # https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsnippet
-    text_snippet = automl.TextSnippet(
-        content=content, mime_type="text/plain"
-    )
+    text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
     payload = automl.ExamplePayload(text_snippet=text_snippet)
 
     response = prediction_client.predict(name=model_full_id, payload=payload)
 
     for annotation_payload in response.payload:
-        print(
-            "Text Extract Entity Types: {}".format(
-                annotation_payload.display_name
-            )
-        )
-        print(
-            "Text Score: {}".format(annotation_payload.text_extraction.score)
-        )
+        print("Text Extract Entity Types: {}".format(annotation_payload.display_name))
+        print("Text Score: {}".format(annotation_payload.text_extraction.score))
         text_segment = annotation_payload.text_extraction.text_segment
         print("Text Extract Entity Content: {}".format(text_segment.content))
         print("Text Start Offset: {}".format(text_segment.start_offset))
diff --git a/automl/snippets/language_sentiment_analysis_create_dataset_test.py b/automl/snippets/language_sentiment_analysis_create_dataset_test.py
index 1ac54461984f..1065c88099ae 100644
--- a/automl/snippets/language_sentiment_analysis_create_dataset_test.py
+++ b/automl/snippets/language_sentiment_analysis_create_dataset_test.py
@@ -25,17 +25,13 @@
 
 def test_sentiment_analysis_create_dataset(capsys):
     dataset_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    language_sentiment_analysis_create_dataset.create_dataset(
-        PROJECT_ID, dataset_name
-    )
+    language_sentiment_analysis_create_dataset.create_dataset(PROJECT_ID, dataset_name)
     out, _ = capsys.readouterr()
     assert "Dataset id: " in out
 
     # Delete the created dataset
     dataset_id = out.splitlines()[1].split()[2]
     client = automl.AutoMlClient()
-    dataset_full_id = client.dataset_path(
-        PROJECT_ID, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(PROJECT_ID, "us-central1", dataset_id)
     response = client.delete_dataset(name=dataset_full_id)
     response.result()
diff --git a/automl/snippets/language_sentiment_analysis_predict.py b/automl/snippets/language_sentiment_analysis_predict.py
index e2f5c77799a2..8036d262540d 100644
--- a/automl/snippets/language_sentiment_analysis_predict.py
+++ b/automl/snippets/language_sentiment_analysis_predict.py
@@ -26,23 +26,17 @@ def predict(project_id, model_id, content):
     prediction_client = automl.PredictionServiceClient()
 
     # Get the full path of the model.
-    model_full_id = automl.AutoMlClient.model_path(
-        project_id, "us-central1", model_id
-    )
+    model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
 
     # Supported mime_types: 'text/plain', 'text/html'
     # https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsnippet
-    text_snippet = automl.TextSnippet(
-        content=content, mime_type="text/plain"
-    )
+    text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
     payload = automl.ExamplePayload(text_snippet=text_snippet)
 
     response = prediction_client.predict(name=model_full_id, payload=payload)
 
     for annotation_payload in response.payload:
-        print(
-            "Predicted class name: {}".format(annotation_payload.display_name)
-        )
+        print("Predicted class name: {}".format(annotation_payload.display_name))
         print(
             "Predicted sentiment score: {}".format(
                 annotation_payload.text_sentiment.sentiment
diff --git a/automl/snippets/language_text_classification_create_dataset_test.py b/automl/snippets/language_text_classification_create_dataset_test.py
index a00e6eb167bf..2395e37c7608 100644
--- a/automl/snippets/language_text_classification_create_dataset_test.py
+++ b/automl/snippets/language_text_classification_create_dataset_test.py
@@ -25,17 +25,13 @@
 
 def test_text_classification_create_dataset(capsys):
     dataset_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    language_text_classification_create_dataset.create_dataset(
-        PROJECT_ID, dataset_name
-    )
+    language_text_classification_create_dataset.create_dataset(PROJECT_ID, dataset_name)
     out, _ = capsys.readouterr()
     assert "Dataset id: " in out
 
     # Delete the created dataset
     dataset_id = out.splitlines()[1].split()[2]
     client = automl.AutoMlClient()
-    dataset_full_id = client.dataset_path(
-        PROJECT_ID, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(PROJECT_ID, "us-central1", dataset_id)
     response = client.delete_dataset(name=dataset_full_id)
     response.result()
diff --git a/automl/snippets/language_text_classification_create_model.py b/automl/snippets/language_text_classification_create_model.py
index ed996def9d2c..00b5114f0125 100644
--- a/automl/snippets/language_text_classification_create_model.py
+++ b/automl/snippets/language_text_classification_create_model.py
@@ -38,6 +38,6 @@ def create_model(project_id, dataset_id, display_name):
     # Create a model with the model metadata in the region.
     response = client.create_model(parent=project_location, model=model)
 
-    print(u"Training operation name: {}".format(response.operation.name))
+    print("Training operation name: {}".format(response.operation.name))
     print("Training started...")
     # [END automl_language_text_classification_create_model]
diff --git a/automl/snippets/language_text_classification_predict.py b/automl/snippets/language_text_classification_predict.py
index 4baa847850e1..cfc4a6e43780 100644
--- a/automl/snippets/language_text_classification_predict.py
+++ b/automl/snippets/language_text_classification_predict.py
@@ -26,26 +26,18 @@ def predict(project_id, model_id, content):
     prediction_client = automl.PredictionServiceClient()
 
     # Get the full path of the model.
-    model_full_id = automl.AutoMlClient.model_path(
-        project_id, "us-central1", model_id
-    )
+    model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
 
     # Supported mime_types: 'text/plain', 'text/html'
     # https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsnippet
-    text_snippet = automl.TextSnippet(
-        content=content, mime_type="text/plain"
-    )
+    text_snippet = automl.TextSnippet(content=content, mime_type="text/plain")
     payload = automl.ExamplePayload(text_snippet=text_snippet)
 
     response = prediction_client.predict(name=model_full_id, payload=payload)
 
     for annotation_payload in response.payload:
+        print(u"Predicted class name: {}".format(annotation_payload.display_name))
         print(
-            u"Predicted class name: {}".format(annotation_payload.display_name)
-        )
-        print(
-            u"Predicted class score: {}".format(
-                annotation_payload.classification.score
-            )
+            u"Predicted class score: {}".format(annotation_payload.classification.score)
         )
     # [END automl_language_text_classification_predict]
diff --git a/automl/snippets/list_model_evaluations.py b/automl/snippets/list_model_evaluations.py
index c5e29d7081dd..8037553c3ccd 100644
--- a/automl/snippets/list_model_evaluations.py
+++ b/automl/snippets/list_model_evaluations.py
@@ -34,17 +34,9 @@ def list_model_evaluations(project_id, model_id):
     print("List of model evaluations:")
     for evaluation in client.list_model_evaluations(parent=model_full_id, filter=""):
         print("Model evaluation name: {}".format(evaluation.name))
-        print(
-            "Model annotation spec id: {}".format(
-                evaluation.annotation_spec_id
-            )
-        )
+        print("Model annotation spec id: {}".format(evaluation.annotation_spec_id))
         print("Create Time: {}".format(evaluation.create_time))
-        print(
-            "Evaluation example count: {}".format(
-                evaluation.evaluated_example_count
-            )
-        )
+        print("Evaluation example count: {}".format(evaluation.evaluated_example_count))
         # [END automl_language_sentiment_analysis_list_model_evaluations]
         # [END automl_language_text_classification_list_model_evaluations]
         # [END automl_translate_list_model_evaluations]
diff --git a/automl/snippets/list_models.py b/automl/snippets/list_models.py
index d46ef1046717..80913105af17 100644
--- a/automl/snippets/list_models.py
+++ b/automl/snippets/list_models.py
@@ -31,10 +31,7 @@ def list_models(project_id):
     print("List of models:")
     for model in response:
         # Display the model information.
-        if (
-            model.deployment_state
-            == automl.Model.DeploymentState.DEPLOYED
-        ):
+        if model.deployment_state == automl.Model.DeploymentState.DEPLOYED:
             deployment_state = "deployed"
         else:
             deployment_state = "undeployed"
diff --git a/automl/snippets/model_test.py b/automl/snippets/model_test.py
index da5f806feb56..c64e45ecd591 100644
--- a/automl/snippets/model_test.py
+++ b/automl/snippets/model_test.py
@@ -14,42 +14,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import datetime
 import os
 
-from google.cloud import automl_v1beta1 as automl
-import pytest
-
 import automl_translation_model
 
 project_id = os.environ["GOOGLE_CLOUD_PROJECT"]
 compute_region = "us-central1"
 
 
-@pytest.mark.skip(reason="creates too many models")
-def test_model_create_status_delete(capsys):
-    # create model
-    client = automl.AutoMlClient()
-    model_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    project_location = f"projects/{project_id}/locations/{compute_region}"
-    my_model = {
-        "display_name": model_name,
-        "dataset_id": "3876092572857648864",
-        "translation_model_metadata": {"base_model": ""},
-    }
-    response = client.create_model(parent=project_location, model=my_model)
-    operation_name = response.operation.name
-    assert operation_name
-
-    # get operation status
-    automl_translation_model.get_operation_status(operation_name)
-    out, _ = capsys.readouterr()
-    assert "Operation status: " in out
-
-    # cancel operation
-    response.cancel()
-
-
 def test_model_list_get_evaluate(capsys):
     # list models
     automl_translation_model.list_models(project_id, compute_region, "")
@@ -62,19 +34,3 @@ def test_model_list_get_evaluate(capsys):
     automl_translation_model.get_model(project_id, compute_region, model_id)
     out, _ = capsys.readouterr()
     assert "Model name: " in out
-
-    # list model evaluations
-    automl_translation_model.list_model_evaluations(
-        project_id, compute_region, model_id, ""
-    )
-    out, _ = capsys.readouterr()
-    list_evals_output = out.splitlines()
-    assert "name: " in list_evals_output[1]
-
-    # get model evaluation
-    model_evaluation_id = list_evals_output[1].split("/")[-1][:-1]
-    automl_translation_model.get_model_evaluation(
-        project_id, compute_region, model_id, model_evaluation_id
-    )
-    out, _ = capsys.readouterr()
-    assert model_evaluation_id in out
diff --git a/automl/snippets/noxfile.py b/automl/snippets/noxfile.py
index bca0522ec4d9..bbd25fcdb5e7 100644
--- a/automl/snippets/noxfile.py
+++ b/automl/snippets/noxfile.py
@@ -38,28 +38,25 @@
 
 TEST_CONFIG = {
     # You can opt out from the test for specific Python versions.
-    'ignored_versions': ["2.7"],
-
+    "ignored_versions": ["2.7"],
     # Old samples are opted out of enforcing Python type hints
     # All new samples should feature them
-    'enforce_type_hints': False,
-
+    "enforce_type_hints": False,
     # An envvar key for determining the project id to use. Change it
     # to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a
     # build specific Cloud project. You can also use your own string
     # to use your own Cloud project.
-    'gcloud_project_env': 'GOOGLE_CLOUD_PROJECT',
+    "gcloud_project_env": "GOOGLE_CLOUD_PROJECT",
     # 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT',
-
     # A dictionary you want to inject into your test. Don't put any
     # secrets here. These values will override predefined values.
-    'envs': {},
+    "envs": {},
 }
 
 
 try:
     # Ensure we can import noxfile_config in the project's directory.
-    sys.path.append('.')
+    sys.path.append(".")
     from noxfile_config import TEST_CONFIG_OVERRIDE
 except ImportError as e:
     print("No user noxfile_config found: detail: {}".format(e))
@@ -74,12 +71,12 @@ def get_pytest_env_vars() -> Dict[str, str]:
     ret = {}
 
     # Override the GCLOUD_PROJECT and the alias.
-    env_key = TEST_CONFIG['gcloud_project_env']
+    env_key = TEST_CONFIG["gcloud_project_env"]
     # This should error out if not set.
-    ret['GOOGLE_CLOUD_PROJECT'] = os.environ[env_key]
+    ret["GOOGLE_CLOUD_PROJECT"] = os.environ[env_key]
 
     # Apply user supplied envs.
-    ret.update(TEST_CONFIG['envs'])
+    ret.update(TEST_CONFIG["envs"])
     return ret
 
 
@@ -88,7 +85,7 @@ def get_pytest_env_vars() -> Dict[str, str]:
 ALL_VERSIONS = ["2.7", "3.6", "3.7", "3.8"]
 
 # Any default versions that should be ignored.
-IGNORED_VERSIONS = TEST_CONFIG['ignored_versions']
+IGNORED_VERSIONS = TEST_CONFIG["ignored_versions"]
 
 TESTED_VERSIONS = sorted([v for v in ALL_VERSIONS if v not in IGNORED_VERSIONS])
 
@@ -137,7 +134,7 @@ def _determine_local_import_names(start_dir: str) -> List[str]:
 
 @nox.session
 def lint(session: nox.sessions.Session) -> None:
-    if not TEST_CONFIG['enforce_type_hints']:
+    if not TEST_CONFIG["enforce_type_hints"]:
         session.install("flake8", "flake8-import-order")
     else:
         session.install("flake8", "flake8-import-order", "flake8-annotations")
@@ -146,9 +143,11 @@ def lint(session: nox.sessions.Session) -> None:
     args = FLAKE8_COMMON_ARGS + [
         "--application-import-names",
         ",".join(local_names),
-        "."
+        ".",
     ]
     session.run("flake8", *args)
+
+
 #
 # Black
 #
@@ -161,6 +160,7 @@ def blacken(session: nox.sessions.Session) -> None:
 
     session.run("black", *python_files)
 
+
 #
 # Sample Tests
 #
@@ -169,7 +169,9 @@ def blacken(session: nox.sessions.Session) -> None:
 PYTEST_COMMON_ARGS = ["--junitxml=sponge_log.xml"]
 
 
-def _session_tests(session: nox.sessions.Session, post_install: Callable = None) -> None:
+def _session_tests(
+    session: nox.sessions.Session, post_install: Callable = None
+) -> None:
     """Runs py.test for a particular project."""
     if os.path.exists("requirements.txt"):
         session.install("-r", "requirements.txt")
@@ -200,9 +202,9 @@ def py(session: nox.sessions.Session) -> None:
     if session.python in TESTED_VERSIONS:
         _session_tests(session)
     else:
-        session.skip("SKIPPED: {} tests are disabled for this sample.".format(
-            session.python
-        ))
+        session.skip(
+            "SKIPPED: {} tests are disabled for this sample.".format(session.python)
+        )
 
 
 #
diff --git a/automl/snippets/predict_test.py b/automl/snippets/predict_test.py
deleted file mode 100644
index d00a4658d7bb..000000000000
--- a/automl/snippets/predict_test.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-
-import automl_translation_predict
-
-project_id = os.environ["GOOGLE_CLOUD_PROJECT"]
-compute_region = "us-central1"
-
-
-def test_predict(capsys):
-    model_id = "TRL3128559826197068699"
-    automl_translation_predict.predict(
-        project_id, compute_region, model_id, "resources/input.txt"
-    )
-    out, _ = capsys.readouterr()
-    assert "Translated content: " in out
diff --git a/automl/snippets/requirements.txt b/automl/snippets/requirements.txt
index 6ca3167b4e5d..e16fe8f2bbb2 100644
--- a/automl/snippets/requirements.txt
+++ b/automl/snippets/requirements.txt
@@ -1,3 +1,3 @@
 google-cloud-translate==3.0.2
 google-cloud-storage==1.33.0
-google-cloud-automl==2.0.0
+google-cloud-automl==2.1.0
diff --git a/automl/snippets/translate_create_dataset_test.py b/automl/snippets/translate_create_dataset_test.py
index 2f6dd2238f47..d25f0f8e55f6 100644
--- a/automl/snippets/translate_create_dataset_test.py
+++ b/automl/snippets/translate_create_dataset_test.py
@@ -33,8 +33,6 @@ def test_translate_create_dataset(capsys):
     # Delete the created dataset
     dataset_id = out.splitlines()[1].split()[2]
     client = automl.AutoMlClient()
-    dataset_full_id = client.dataset_path(
-        PROJECT_ID, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(PROJECT_ID, "us-central1", dataset_id)
     response = client.delete_dataset(name=dataset_full_id)
     response.result()
diff --git a/automl/snippets/translate_predict.py b/automl/snippets/translate_predict.py
index 455603198c36..9b93f28485eb 100644
--- a/automl/snippets/translate_predict.py
+++ b/automl/snippets/translate_predict.py
@@ -26,9 +26,7 @@ def predict(project_id, model_id, file_path):
     prediction_client = automl.PredictionServiceClient()
 
     # Get the full path of the model.
-    model_full_id = automl.AutoMlClient.model_path(
-        project_id, "us-central1", model_id
-    )
+    model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
 
     # Read the file content for translation.
     with open(file_path, "rb") as content_file:
diff --git a/automl/snippets/vision_classification_create_dataset_test.py b/automl/snippets/vision_classification_create_dataset_test.py
index efd32810ab53..7b86f42e4c15 100644
--- a/automl/snippets/vision_classification_create_dataset_test.py
+++ b/automl/snippets/vision_classification_create_dataset_test.py
@@ -28,17 +28,13 @@
 def test_vision_classification_create_dataset(capsys):
     # create dataset
     dataset_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    vision_classification_create_dataset.create_dataset(
-        PROJECT_ID, dataset_name
-    )
+    vision_classification_create_dataset.create_dataset(PROJECT_ID, dataset_name)
     out, _ = capsys.readouterr()
     assert "Dataset id: " in out
 
     # Delete the created dataset
     dataset_id = out.splitlines()[1].split()[2]
     client = automl.AutoMlClient()
-    dataset_full_id = client.dataset_path(
-        PROJECT_ID, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(PROJECT_ID, "us-central1", dataset_id)
     response = client.delete_dataset(name=dataset_full_id)
     response.result()
diff --git a/automl/snippets/vision_classification_deploy_model_node_count.py b/automl/snippets/vision_classification_deploy_model_node_count.py
index 98be955c3392..3884477d6727 100644
--- a/automl/snippets/vision_classification_deploy_model_node_count.py
+++ b/automl/snippets/vision_classification_deploy_model_node_count.py
@@ -28,13 +28,10 @@ def deploy_model(project_id, model_id):
 
     # node count determines the number of nodes to deploy the model on.
     # https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#imageclassificationmodeldeploymentmetadata
-    metadata = automl.ImageClassificationModelDeploymentMetadata(
-        node_count=2
-    )
+    metadata = automl.ImageClassificationModelDeploymentMetadata(node_count=2)
 
     request = automl.DeployModelRequest(
-        name=model_full_id,
-        image_classification_model_deployment_metadata=metadata
+        name=model_full_id, image_classification_model_deployment_metadata=metadata
     )
     response = client.deploy_model(request=request)
 
diff --git a/automl/snippets/vision_classification_deploy_model_node_count_test.py b/automl/snippets/vision_classification_deploy_model_node_count_test.py
index 3f6ff430a4d2..c565b58de059 100644
--- a/automl/snippets/vision_classification_deploy_model_node_count_test.py
+++ b/automl/snippets/vision_classification_deploy_model_node_count_test.py
@@ -28,9 +28,7 @@ def test_classification_deploy_model_with_node_count(capsys):
     # nonexistent model and confirm that the model was not found, but other
     # elements of the request were valid.
     try:
-        vision_classification_deploy_model_node_count.deploy_model(
-            PROJECT_ID, MODEL_ID
-        )
+        vision_classification_deploy_model_node_count.deploy_model(PROJECT_ID, MODEL_ID)
         out, _ = capsys.readouterr()
         assert "The model does not exist" in out
     except Exception as e:
diff --git a/automl/snippets/vision_classification_predict.py b/automl/snippets/vision_classification_predict.py
index 4b1a2f7a8394..355de0bc08ad 100644
--- a/automl/snippets/vision_classification_predict.py
+++ b/automl/snippets/vision_classification_predict.py
@@ -26,9 +26,7 @@ def predict(project_id, model_id, file_path):
     prediction_client = automl.PredictionServiceClient()
 
     # Get the full path of the model.
-    model_full_id = automl.AutoMlClient.model_path(
-        project_id, "us-central1", model_id
-    )
+    model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
 
     # Read the file.
     with open(file_path, "rb") as content_file:
@@ -42,11 +40,7 @@ def predict(project_id, model_id, file_path):
     # https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
     params = {"score_threshold": "0.8"}
 
-    request = automl.PredictRequest(
-        name=model_full_id,
-        payload=payload,
-        params=params
-    )
+    request = automl.PredictRequest(name=model_full_id, payload=payload, params=params)
     response = prediction_client.predict(request=request)
 
     print("Prediction results:")
diff --git a/automl/snippets/vision_object_detection_create_dataset_test.py b/automl/snippets/vision_object_detection_create_dataset_test.py
index e7a82e3a9e03..97cff120b6d6 100644
--- a/automl/snippets/vision_object_detection_create_dataset_test.py
+++ b/automl/snippets/vision_object_detection_create_dataset_test.py
@@ -28,17 +28,13 @@
 def test_vision_object_detection_create_dataset(capsys):
     # create dataset
     dataset_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
-    vision_object_detection_create_dataset.create_dataset(
-        PROJECT_ID, dataset_name
-    )
+    vision_object_detection_create_dataset.create_dataset(PROJECT_ID, dataset_name)
     out, _ = capsys.readouterr()
     assert "Dataset id: " in out
 
     # Delete the created dataset
     dataset_id = out.splitlines()[1].split()[2]
     client = automl.AutoMlClient()
-    dataset_full_id = client.dataset_path(
-        PROJECT_ID, "us-central1", dataset_id
-    )
+    dataset_full_id = client.dataset_path(PROJECT_ID, "us-central1", dataset_id)
     response = client.delete_dataset(name=dataset_full_id)
     response.result()
diff --git a/automl/snippets/vision_object_detection_deploy_model_node_count.py b/automl/snippets/vision_object_detection_deploy_model_node_count.py
index 9a15d2287b97..cc65941b1027 100644
--- a/automl/snippets/vision_object_detection_deploy_model_node_count.py
+++ b/automl/snippets/vision_object_detection_deploy_model_node_count.py
@@ -28,9 +28,7 @@ def deploy_model(project_id, model_id):
 
     # node count determines the number of nodes to deploy the model on.
     # https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#imageobjectdetectionmodeldeploymentmetadata
-    metadata = automl.ImageObjectDetectionModelDeploymentMetadata(
-        node_count=2
-    )
+    metadata = automl.ImageObjectDetectionModelDeploymentMetadata(node_count=2)
 
     request = automl.DeployModelRequest(
         name=model_full_id,
diff --git a/automl/snippets/vision_object_detection_predict.py b/automl/snippets/vision_object_detection_predict.py
index 2a059d40396a..51ed0ac029fa 100644
--- a/automl/snippets/vision_object_detection_predict.py
+++ b/automl/snippets/vision_object_detection_predict.py
@@ -26,9 +26,7 @@ def predict(project_id, model_id, file_path):
     prediction_client = automl.PredictionServiceClient()
 
     # Get the full path of the model.
-    model_full_id = automl.AutoMlClient.model_path(
-        project_id, "us-central1", model_id
-    )
+    model_full_id = automl.AutoMlClient.model_path(project_id, "us-central1", model_id)
 
     # Read the file.
     with open(file_path, "rb") as content_file:
@@ -42,21 +40,13 @@ def predict(project_id, model_id, file_path):
     # https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#predictrequest
     params = {"score_threshold": "0.8"}
 
-    request = automl.PredictRequest(
-        name=model_full_id,
-        payload=payload,
-        params=params
-    )
+    request = automl.PredictRequest(name=model_full_id, payload=payload, params=params)
 
     response = prediction_client.predict(request=request)
     print("Prediction results:")
     for result in response.payload:
         print("Predicted class name: {}".format(result.display_name))
-        print(
-            "Predicted class score: {}".format(
-                result.image_object_detection.score
-            )
-        )
+        print("Predicted class score: {}".format(result.image_object_detection.score))
         bounding_box = result.image_object_detection.bounding_box
         print("Normalized Vertices:")
         for vertex in bounding_box.normalized_vertices:
diff --git a/automl/tables/requirements.txt b/automl/tables/requirements.txt
index d2157cd180ba..94f5d6c85200 100644
--- a/automl/tables/requirements.txt
+++ b/automl/tables/requirements.txt
@@ -1 +1 @@
-google-cloud-automl==2.0.0
+google-cloud-automl==2.1.0