fix: auto review (#793)

* feat: tune template

* feat: tune template

* fix: auto review

* fix: auto review

* fix: auto review

* fix: auto review

* fix: auti review

* fix: auti review

* fix: auti review

* fix: auti review

notebooks/community/ml_ops/stage3/get_started_with_airflow_and_vertex_pipelines.ipynb

@@ -64,17 +64,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with Apache Airflow and Vertex AI Pipelines."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:flowers,icn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is [Condensed Game Data](gs://example-datasets/game_data_condensed.csv), which comes from the [Apache Beam examples](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/complete/game). The version used in this tutorial is stored in a Cloud Storage bucket."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -97,8 +86,26 @@
         "- Create Cloud Composer environment.\n",
         "- Upload Airflow DAG to Composer environment that performs data processing -- i.e., creates a BigQuery table from a CSV file.\n",
         "- Create a `Vertex AI Pipeline` that triggers the Airflow DAG.\n",
-        "- Execute the `Vertex AI Pipeline`.\n",
+        "- Execute the `Vertex AI Pipeline`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:flowers,icn"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "The dataset used for this tutorial is [Condensed Game Data](gs://example-datasets/game_data_condensed.csv), which comes from the [Apache Beam examples](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/complete/game). The version used in this tutorial is stored in a Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8a374d1a7dc"
+      },
+      "source": [
         "### Costs\n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",
@@ -747,7 +754,7 @@
       "source": [
         "# This code is modified version of https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/composer/rest/get_client_id.py\n",
         "\n",
-        "shell_output=! python3 get_composer_config.py $PROJECT_ID $REGION $COMPOSER_ENV_NAME\n",
+        "shell_output = ! python3 get_composer_config.py $PROJECT_ID $REGION $COMPOSER_ENV_NAME\n",
         "COMPOSER_WEB_URI = shell_output[0]\n",
         "COMPOSER_DAG_GCS = shell_output[1]\n",
         "COMPOSER_CLIENT_ID = shell_output[2]\n",
@@ -977,7 +984,7 @@
         "    dag_name: str,\n",
         "    composer_client_id: str,\n",
         "    composer_webserver_id: str,\n",
-        "    response: Output[Artifact]\n",
+        "    response: Output[Artifact],\n",
         "):\n",
         "    # [START composer_trigger]\n",
         "\n",
@@ -988,10 +995,9 @@
         "    from google.auth.transport.requests import Request\n",
         "    from google.oauth2 import id_token\n",
         "\n",
+        "    IAM_SCOPE = \"https://www.googleapis.com/auth/iam\"\n",
+        "    OAUTH_TOKEN_URI = \"https://www.googleapis.com/oauth2/v4/token\"\n",
         "\n",
-        "    IAM_SCOPE = 'https://www.googleapis.com/auth/iam'\n",
-        "    OAUTH_TOKEN_URI = 'https://www.googleapis.com/oauth2/v4/token'\n",
-        "    \n",
         "    data = '{\"replace_microseconds\":\"false\"}'\n",
         "    context = None\n",
         "\n",
@@ -1008,13 +1014,13 @@
         "    \"\"\"\n",
         "\n",
         "    # Form webserver URL to make REST API calls\n",
-        "    webserver_url = f'{composer_webserver_id}/api/experimental/dags/{dag_name}/dag_runs'\n",
+        "    webserver_url = f\"{composer_webserver_id}/api/experimental/dags/{dag_name}/dag_runs\"\n",
         "    # print(webserver_url)\n",
         "\n",
         "    # This code is copied from\n",
         "    # https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/iap/make_iap_request.py\n",
         "    # START COPIED IAP CODE\n",
-        "    def make_iap_request(url, client_id, method='GET', **kwargs):\n",
+        "    def make_iap_request(url, client_id, method=\"GET\", **kwargs):\n",
         "        \"\"\"Makes a request to an application protected by Identity-Aware Proxy.\n",
         "        Args:\n",
         "          url: The Identity-Aware Proxy-protected URL to fetch.\n",
@@ -1028,8 +1034,8 @@
         "          The page body, or raises an exception if the page couldn't be retrieved.\n",
         "        \"\"\"\n",
         "        # Set the default timeout, if missing\n",
-        "        if 'timeout' not in kwargs:\n",
+        "        if \"timeout\" not in kwargs:\n",
-        "            kwargs['timeout'] = 90\n",
+        "            kwargs[\"timeout\"] = 90\n",
         "\n",
         "        # Obtain an OpenID Connect (OIDC) token from metadata server or using service\n",
         "        # account.\n",
@@ -1039,32 +1045,41 @@
         "        # Authorization header containing \"Bearer \" followed by a\n",
         "        # Google-issued OpenID Connect token for the service account.\n",
         "        resp = requests.request(\n",
-        "            method, url,\n",
+        "            method,\n",
-        "            headers={'Authorization': 'Bearer {}'.format(\n",
+        "            url,\n",
-        "                google_open_id_connect_token)}, **kwargs)\n",
+        "            headers={\"Authorization\": \"Bearer {}\".format(google_open_id_connect_token)},\n",
+        "            **kwargs,\n",
+        "        )\n",
         "        if resp.status_code == 403:\n",
-        "            raise Exception('Service account does not have permission to '\n",
+        "            raise Exception(\n",
-        "                            'access the IAP-protected application.')\n",
+        "                \"Service account does not have permission to \"\n",
+        "                \"access the IAP-protected application.\"\n",
+        "            )\n",
         "        elif resp.status_code != 200:\n",
         "            raise Exception(\n",
-        "                'Bad response from application: {!r} / {!r} / {!r}'.format(\n",
+        "                \"Bad response from application: {!r} / {!r} / {!r}\".format(\n",
-        "                    resp.status_code, resp.headers, resp.text))\n",
+        "                    resp.status_code, resp.headers, resp.text\n",
+        "                )\n",
+        "            )\n",
         "        else:\n",
         "            print(f\"response = {resp.text}\")\n",
         "            # not executed when testing locally\n",
         "            if response:\n",
         "                file_path = os.path.join(response.path)\n",
         "                os.makedirs(file_path)\n",
-        "                with open(os.path.join(file_path, \"airflow_response.json\"), 'w') as f:\n",
+        "                with open(os.path.join(file_path, \"airflow_response.json\"), \"w\") as f:\n",
         "                    json.dump(resp.text, f)\n",
         "\n",
         "    # END COPIED IAP CODE\n",
         "\n",
-        "    \n",
         "    # Make a POST request to IAP which then Triggers the DAG\n",
         "    make_iap_request(\n",
-        "        webserver_url, composer_client_id, method='POST', json={\"conf\": data, \"replace_microseconds\": 'false'})\n",
+        "        webserver_url,\n",
-        "    \n",
+        "        composer_client_id,\n",
+        "        method=\"POST\",\n",
+        "        json={\"conf\": data, \"replace_microseconds\": \"false\"},\n",
+        "    )\n",
+        "\n",
         "    # [END composer_trigger]"
       ]
     },
@@ -1094,7 +1109,7 @@
         "        dag_name=COMPOSER_DAG_NAME,\n",
         "        composer_client_id=COMPOSER_CLIENT_ID,\n",
         "        composer_webserver_id=COMPOSER_WEB_URI,\n",
-        "        response=None\n",
+        "        response=None,\n",
         "    )\n",
         "except Exception as e:\n",
         "    print(e)"
@@ -1121,12 +1136,13 @@
       },
       "outputs": [],
       "source": [
-        "PATH=%env PATH\n",
+        "PATH = %env PATH\n",
         "%env PATH={PATH}:/home/jupyter/.local/bin\n",
         "\n",
         "PIPELINE_ROOT = f\"{BUCKET_URI}/pipeline_root/\"\n",
         "print(PIPELINE_ROOT)\n",
         "\n",
+        "\n",
         "@dsl.pipeline(\n",
         "    name=\"pipeline-trigger-airflow-dag\",\n",
         "    description=\"Trigger Airflow DAG from Vertex AI Pipelines\",\n",
@@ -1140,7 +1156,7 @@
         "    data_processing_task = trigger_airflow_dag(\n",
         "        dag_name=data_processing_task_dag_name,\n",
         "        composer_client_id=COMPOSER_CLIENT_ID,\n",
-        "        composer_webserver_id=COMPOSER_WEB_URI\n",
+        "        composer_webserver_id=COMPOSER_WEB_URI,\n",
         "    )"
       ]
     },
@@ -1171,9 +1187,8 @@
         "    display_name=\"airflow_pipeline\",\n",
         "    template_path=\"pipeline-trigger-airflow-dag.json\",\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
-        "    parameter_values={\n",
+        "    parameter_values={},\n",
-        "    },\n",
+        "    enable_caching=False,\n",
-        "    enable_caching=False\n",
         ")\n",
         "\n",
         "pipeline.run()\n",
@@ -1213,7 +1228,7 @@
       },
       "outputs": [],
       "source": [
-        "COMPOSER_WEB_URI + '/admin/airflow/tree?dag_id=dag_gcs_to_bq_orch'"
+        "COMPOSER_WEB_URI + \"/admin/airflow/tree?dag_id=dag_gcs_to_bq_orch\""
       ]
     },
     {

notebooks/community/ml_ops/stage3/get_started_with_automl_pipeline_components.ipynb

@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with AutoML pipeline components."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:flowers,icn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower in the given image from the five classes of flowers: daisy, dandelion, rose, sunflower, or tulip."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -100,8 +89,26 @@
         "    - Training a Vertex AI AutoML trained model.\n",
         "    - Test the serving binary with a batch prediction job.\n",
         "    - Deploying a Vertex AI AutoML trained model.\n",
-        "- Execute a Vertex AI pipeline.\n",
+        "- Execute a Vertex AI pipeline.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:flowers,icn"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower in the given image from the five classes of flowers: daisy, dandelion, rose, sunflower, or tulip."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eef426a35e17"
+      },
+      "source": [
         "### Costs\n",
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",

notebooks/community/ml_ops/stage3/get_started_with_automl_tabular_pipeline_workflow.ipynb

@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with AutoML Tabular pipeline template."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:iris,lcn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Iris dataset](https://www.tensorflow.org/datasets/catalog/iris) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of Iris flower species from a class of three species: setosa, virginica, or versicolor."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -105,8 +94,26 @@
         "- Export AutoML model as an OSS TF model.\n",
         "- Create `Endpoint` resource.\n",
         "- Deploy exported OSS TF model.\n",
-        "- Make a prediction.\n",
+        "- Make a prediction."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:iris,lcn"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "The dataset used for this tutorial is the [Iris dataset](https://www.tensorflow.org/datasets/catalog/iris) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of Iris flower species from a class of three species: setosa, virginica, or versicolor."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4fc0ad661ebb"
+      },
+      "source": [
         "### Costs \n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",

notebooks/community/ml_ops/stage3/get_started_with_bq_tfdv_pipeline_components.ipynb

@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with BigQuery and TFDV pipeline components."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:gsod,lrg"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -99,26 +88,31 @@
         "- Execute a Vertex AI pipeline."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:gsod,lrg"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "0c997d8d92ce"
       },
       "source": [
-        "### Costs \n",
+        "### Costs\n",
-        "\n",
-        "\n",
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",
-        "* Vertex AI\n",
+        "- Vertex AI\n",
-        "* Cloud Storage\n",
+        "- Cloud Storage\n",
+        "- BigQuery\n",
         "\n",
-        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
       ]
     },
     {

notebooks/community/ml_ops/stage3/get_started_with_bqml_pipeline_components.ipynb

@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with BigQuery ML pipeline components."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:penguins,lcn,bq"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the Penguins dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset predicts the species."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -105,26 +94,31 @@
         "- Make a prediction with the deployed Vertex AI model."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:penguins,lcn,bq"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the Penguins dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset predicts the species."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "0c997d8d92ce"
       },
       "source": [
-        "### Costs \n",
+        "### Costs\n",
-        "\n",
-        "\n",
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",
-        "* Vertex AI\n",
+        "- Vertex AI\n",
-        "* Cloud Storage\n",
+        "- Cloud Storage\n",
+        "- BigQuery\n",
         "\n",
-        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and [BigQuery pricing](https://cloud.google.com/bigquery/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
-        "to generate a cost estimate based on your projected usage."
       ]
     },
     {

notebooks/community/ml_ops/stage3/get_started_with_custom_training_pipeline_components.ipynb

@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with custom training pipeline components."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:flowers,icn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower an image is from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -109,6 +98,17 @@
         "- Execute a Vertex AI pipeline."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:flowers,icn"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower an image is from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/community/ml_ops/stage3/get_started_with_dataflow_pipeline_components.ipynb

@@ -67,17 +67,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with Dataflow pipeline components."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:gsod,lrg"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -101,6 +90,39 @@
         "- Execute a Vertex AI pipeline."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:gsod,lrg"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0c997d8d92ce"
+      },
+      "source": [
+        "### Costs \n",
+        "\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "\n",
+        "Learn about [Vertex AI\n",
+        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
+        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
+        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/community/ml_ops/stage3/get_started_with_dataproc_serverless_pipeline_components.ipynb

@@ -85,8 +85,26 @@
         "- `DataprocPySparkBatchOp` for running PySpark batch workloads.\n",
         "- `DataprocSparkBatchOp` for running Spark batch workloads.\n",
         "- `DataprocSparkSqlBatchOp` for running Spark SQL batch workloads.\n",
-        "- `DataprocSparkRBatchOp` for running SparkR batch workloads.\n",
+        "- `DataprocSparkRBatchOp` for running SparkR batch workloads."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4ced09c1b4ce"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "No dataset is used in this tutorial. References to an example dataset are for demonstration purposes."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "25697c6fccd3"
+      },
+      "source": [
         "### Costs\n",
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",

notebooks/community/ml_ops/stage3/get_started_with_hpt_pipeline_components.ipynb

@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with Hyperparameter Tuning pipeline components."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:horses_or_humans,icn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Horses or Humans](https://www.tensorflow.org/datasets/catalog/horses_or_humans) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The trained model predicts whether an image is a horse or human being."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -100,8 +89,26 @@
         "    - If the metrics exceed a specified threshold.\n",
         "      - Get the location of the model artifacts for the best tuned model.\n",
         "      - Upload the model artifacts to a `Vertex AI Model` resource.\n",
-        "- Execute a Vertex AI pipeline.\n",
+        "- Execute a Vertex AI pipeline."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:horses_or_humans,icn"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "The dataset used for this tutorial is the [Horses or Humans](https://www.tensorflow.org/datasets/catalog/horses_or_humans) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The trained model predicts whether an image is a horse or human being."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5e2eba58ad71"
+      },
+      "source": [
         "### Costs \n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",

notebooks/community/ml_ops/stage3/get_started_with_kubeflow_pipelines.ipynb

@@ -87,8 +87,26 @@
         "- Executing a KFP pipeline using Vertex AI Pipelines.\n",
         "- Loading component and pipeline definitions from a source code repository.\n",
         "- Building sequential, parallel, multiple output components.\n",
-        "- Building control flow into pipelines.\n",
+        "- Building control flow into pipelines."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4ced09c1b4ce"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "No dataset is used in this tutorial. References to an example dataset are for demonstration purposes."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eef426a35e17"
+      },
+      "source": [
         "### Costs\n",
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",

notebooks/community/ml_ops/stage3/get_started_with_machine_management.ipynb

@@ -65,18 +65,20 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "8b10d2fb975d"
+        "id": "c16c6df3c48d"
       },
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to manage machine resources when training as a component in `Vertex AI Pipelines`.\n",
+        "This tutorial demonstrates how to manage machine resources when training as a component in `Vertex AI Pipelines`."
-        "\n",
+      ]
-        "\n",
+    },
-        "### Dataset\n",
+    {
-        "\n",
+      "cell_type": "markdown",
-        "The dataset is the MNIST dataset. The dataset consists of 28x28 grayscale images of the digits 0 .. 9. \n",
+      "metadata": {
-        "\n",
+        "id": "8b10d2fb975d"
+      },
+      "source": [
         "### Objective\n",
         "\n",
         "In this tutorial, you convert a self-contained custom training component into a `Vertex AI CustomJob`, whereby:\n",
@@ -93,8 +95,26 @@
         "- Create a custom component with a self-contained training job.\n",
         "- Execute pipeline using component-level settings for machine resources\n",
         "- Convert the self-contained training component into a `Vertex AI CustomJob`.\n",
-        "- Execute pipeline using customjob-level settings for machine resources \n",
+        "- Execute pipeline using customjob-level settings for machine resources "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "39c8466c1f07"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "The dataset is the MNIST dataset. The dataset consists of 28x28 grayscale images of the digits 0 .. 9."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "35bee437737d"
+      },
+      "source": [
         "### Costs\n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",

notebooks/community/ml_ops/stage3/get_started_with_rapid_prototyping_bqml_automl.ipynb

@@ -65,6 +65,33 @@
         "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6c75b63ad57e"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to use `Vertex AI Predictions` for rapid prototyping a model.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI Pipelines`\n",
+        "- `Vertex AI AutoML`\n",
+        "- `Vertex AI BigQuery ML`\n",
+        "- `Google Cloud Pipeline Components`\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Creating a BigQuery and Vertex AI training dataset.\n",
+        "- Training a BigQuery ML and AutoML model.\n",
+        "- Extracting evaluation metrics from the BigQueryML and AutoML models.\n",
+        "- Selecting the best trained model.\n",
+        "- Deploying the best trained model.\n",
+        "- Testing the deployed model infrastructure."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -153,33 +180,6 @@
         "</body>\n"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "6c75b63ad57e"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "In this tutorial, you learn how to use `Vertex AI Predictions` for rapid prototyping a model.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `Vertex AI Pipelines`\n",
-        "- `Vertex AI AutoML`\n",
-        "- `Vertex AI BigQuery ML`\n",
-        "- `Google Cloud Pipeline Components`\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- Creating a BigQuery and Vertex AI training dataset.\n",
-        "- Training a BigQuery ML and AutoML model.\n",
-        "- Extracting evaluation metrics from the BigQueryML and AutoML models.\n",
-        "- Selecting the best trained model.\n",
-        "- Deploying the best trained model.\n",
-        "- Testing the deployed model infrastructure."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/community/ml_ops/stage3/get_started_with_tfx_pipeline.ipynb

@@ -75,17 +75,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization: get started with TFX and Vertex AI Pipelines."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:bank,lbn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [CIFAR10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -112,6 +101,17 @@
         "- Execute the pipeline using `Vertex AI Pipelines`."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:bank,lbn"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the [CIFAR10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, or truck."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {

notebooks/community/ml_ops/stage3/mlops_formalization.ipynb

@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 3 : formalization."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:bq,chicago,lbn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Chicago Taxi](https://www.kaggle.com/chicago/chicago-taxi-trips-bq). The version of the dataset you will use in this tutorial is stored in a public BigQuery table. The trained model predicts whether someone would leave a tip for a taxi fare."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -169,6 +158,39 @@
         "    - Deploy the trained `Vertex AI Model` resource to the `Vertex AI Endpoint` resource."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:bq,chicago,lbn"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the [Chicago Taxi](https://www.kaggle.com/chicago/chicago-taxi-trips-bq). The version of the dataset in this tutorial is stored in a public BigQuery table. The trained model predicts whether someone would leave a tip for a taxi fare."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0c997d8d92ce"
+      },
+      "source": [
+        "### Costs \n",
+        "\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* Cloud Storage\n",
+        "\n",
+        "\n",
+        "Learn about [Vertex AI\n",
+        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
+        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
+        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {