Update automl-tabular-classification.ipynb

2026-09-27 23:51:57 +00:00 · 2023-01-09 13:06:36 -06:00
60 changed files with 1145 additions and 22468 deletions
@@ -11,3 +11,4 @@ google-cloud-storage
 google-cloud-build
 ratemate
 GitPython
+google-api-core==2.10
@@ -1,62 +0,0 @@
-# python3 -m pip install "kfp<2.0.0" "google-cloud-aiplatform>=1.16.0" --upgrade --quiet
-from kfp import components
-from kfp.v2 import dsl
-
-# %% Loading components
-upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml')
-deploy_model_to_endpoint_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml')
-transcode_imagedataset_tfrecord_from_csv_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/community-content/pipeline_components/image_ml_model_training/transcode_tfrecord_image_dataset_from_csv/component.yaml')
-load_image_classification_model_from_tfhub_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/b5b65198a6c2ffe8c0fa2aa70127e3325752df68/community-content/pipeline_components/image_ml_model_training/load_image_classification_model/component.yaml')
-preprocess_image_data_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/community-content/pipeline_components/image_ml_model_training/preprocess_image_data/component.yaml')
-train_tensorflow_image_classification_model_op = components.load_component_from_url('https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/community-content/pipeline_components/image_ml_model_training/train_image_classification_model/component.yaml')
-
-
-# %% Pipeline definition
-def image_classification_pipeline():
-    class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']
-    csv_image_data_path = 'gs://cloud-samples-data/ai-platform/flowers/flowers.csv'
-    deploy_model = False
-
-    image_data = dsl.importer(
-        artifact_uri=csv_image_data_path, artifact_class=dsl.Dataset).output
-
-    image_tfrecord_data = transcode_imagedataset_tfrecord_from_csv_op(
-        csv_image_data_path=image_data,
-        class_names=class_names
-    ).outputs['tfrecord_image_data_path']
-
-    loaded_model_outputs = load_image_classification_model_from_tfhub_op(
-        class_names=class_names,
-    ).outputs
-
-    preprocessed_data = preprocess_image_data_op(
-        image_tfrecord_data,
-        height_width_path=loaded_model_outputs['image_size_path'],
-    ).outputs
-
-    trained_model = (train_tensorflow_image_classification_model_op(
-        preprocessed_training_data_path = preprocessed_data['preprocessed_training_data_path'],
-        preprocessed_validation_data_path = preprocessed_data['preprocessed_validation_data_path'],
-        model_path=loaded_model_outputs['loaded_model_path']).
-                   set_cpu_limit('96').
-                   set_memory_limit('128G').
-                   add_node_selector_constraint('cloud.google.com/gke-accelerator', 'NVIDIA_TESLA_A100').
-                   set_gpu_limit('8').
-                   outputs['trained_model_path'])
-
-    vertex_model_name = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op(
-        model=trained_model,
-    ).outputs['model_name']
-
-    # Deploying the model might incur additional costs over time
-    if deploy_model:
-        vertex_endpoint_name = deploy_model_to_endpoint_op(
-            model_name=vertex_model_name,
-        ).outputs['endpoint_name']
-
-pipeline_func = image_classification_pipeline
-
-# %% Pipeline submission
-if __name__ == '__main__':
-    from google.cloud import aiplatform
-    aiplatform.PipelineJob.from_pipeline_func(pipeline_func=pipeline_func).submit()
@@ -30,7 +30,6 @@
 /notebooks/community/neo4j/graph_paysim.ipynb @benofben @laeg
 /notebooks/community/ml_ops/stage1/get_started_with_visionapi_and_vertex_datasets.ipynb @mansari
 /notebooks/community/pipelines/google_cloud_pipeline_components_bqml_pipeline_demand_forecasting.ipynb @inardini
-/notebooks/community/cohere/cohere_embedding_with_matching_engine.ipynb @stewart-co
 /notebooks/community/ml_ops/stage2/get_started_vertex_hpt_r_kernel.ipynb @fhirschmann
 /notebooks/community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.ipynb @fhirschmann
 /notebooks/community/vertex-ai-samples/notebooks/community/model_registry/vertex_ai_model_registry_bqml_custom_model_versioning.ipynb @inardini
@@ -38,6 +37,3 @@
 /notebooks/community/vizier/conversions_vertex_vizier_and_open_source_vizier.ipynb @halio-g
 /notebooks/community/experiments/vertex_ai_model_experimentation.ipynb @inardini @asobran
 /notebooks/community/pipelines/google_cloud_pipeline_components_bqml_pipeline_anomaly_detection.ipynb @inardini
-/notebooks/community/pipelines/google_cloud_pipeline_components_cloud_natural_language_pipeline.ipynb @Narwhalprime
-/notebooks/community/pipelines/google_cloud_pipeline_components_ready_to_go_text_classification_pipeline.ipynb @Narwhalprime
-
@@ -1,3 +0,0 @@
-# README
-
-These are notebooks [Cohere](https://cohere.ai/) built in collaboration with Google.  They demonstrate how to use Cohere's modeling API along with Vertex AI.
@@ -1028,9 +1028,6 @@
        "deployment_resource_pool.dedicated_resources.min_replica_count = MIN_NODES\n",
        "deployment_resource_pool.dedicated_resources.max_replica_count = MAX_NODES\n",
        "deployment_resource_pool.dedicated_resources.machine_spec.machine_type = DEPLOY_COMPUTE\n",
-        "if DEPLOY_NGPU:\n",
-        "    deployment_resource_pool.dedicated_resources.machine_spec.accelerator_type = DEPLOY_GPU\n",
-        "    deployment_resource_pool.dedicated_resources.machine_spec.accelerator_count = DEPLOY_NGPU\n",
        "\n",
        "request = aip_beta.CreateDeploymentResourcePoolRequest(\n",
        "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}\",\n",
@@ -198,7 +198,7 @@
        "if IS_WORKBENCH_NOTEBOOK:\n",
        "    USER_FLAG = \"--user\"\n",
        "\n",
-        "! pip3 install --upgrade tensorflow google-cloud-bigquery google-cloud-aiplatform \"shapely<2\" {USER_FLAG} -q --no-warn-conflicts"
+        "! pip3 install --upgrade tensorflow google-cloud-bigquery google-cloud-aiplatform {USER_FLAG} -q --no-warn-conflicts"
      ]
    },
    {
@@ -72,7 +72,7 @@
      "source": [
        "## Overview\n",
        "\n",
-        "Anomaly detection is the identification of rare observations which deviate significantly from the data using ML. Anomaly detection can be done in many ways. Supervised, unsupervised, graph-based. It is particularly important for certain industries like telecommunications, manufacturing, and financial services.\n",
+        "Anomaly detection is the identification of rare obesrvations which deviate significantly from the data using ML. Anomaly detection can be done in many ways. Supervised, unsupervised, graph-based. It is particularly important for certain industries like telecommunications, manufacturing, and financial services.\n",
        "\n",
        "For instance, in a manufacturing scenario, you may collect some sensor data to predict the number remaining cycles before engine failure (TTF). In this way, you can take actionable decisions about maintenance planning."
      ]
@@ -397,12 +397,13 @@
      "source": [
        "import os\n",
        "\n",
+        "SRC_PATH = \"src\"\n",
        "KFP_COMPONENTS_PATH = \"components\"\n",
        "PIPELINES_PATH = \"pipelines\"\n",
        "TRAIN_PIPELINES_PATH = os.path.join(PIPELINES_PATH, \"train_pipelines\")\n",
        "TEST_PIPELINES_PATH = os.path.join(PIPELINES_PATH, \"test_pipelines\")\n",
        "\n",
-        "! mkdir -m 777 -p {KFP_COMPONENTS_PATH} {TRAIN_PIPELINES_PATH} {TEST_PIPELINES_PATH}"
+        "! mkdir -m 777 -p {SRC_PATH} {KFP_COMPONENTS_PATH} {TRAIN_PIPELINES_PATH} {TEST_PIPELINES_PATH}"
      ]
    },
    {
@@ -424,12 +425,14 @@
      },
      "outputs": [],
      "source": [
+        "from urllib.parse import urlparse\n",
+        "\n",
        "PUBLIC_DATA_URI = (\n",
-        "    \"gs://cloud-samples-data/vertex-ai/pipeline-deployment/datasets/turbofan_anomaly\"\n",
+        "    \" gs://cloud-samples-data/vertex-ai/pipeline-deployment/datasets/turbofan_anomaly\"\n",
        ")\n",
-        "GCS_TRAIN_URI = f\"{PUBLIC_DATA_URI}/train_FD001.csv\"\n",
-        "GCS_TEST_URI = f\"{PUBLIC_DATA_URI}/test_FD001.csv\"\n",
-        "GCS_LABELS_URI = f\"{PUBLIC_DATA_URI}/RUL_FD001.csv\""
+        "GCS_TRAIN_URI = urlparse(PUBLIC_DATA_URI)._replace(path=\"train_FD001.csv\").geturl()\n",
+        "GCS_TEST_URI = urlparse(PUBLIC_DATA_URI)._replace(path=\"test_FD001.csv\").geturl()\n",
+        "GCS_LABELS_URI = urlparse(PUBLIC_DATA_URI)._replace(path=\"RUL_FD001.csv\").geturl()"
      ]
    },
    {
@@ -1475,6 +1478,7 @@
        "# Remove local resorces\n",
        "delete_local_resources = False\n",
        "if delete_local_resources:\n",
+        "    ! rm -rf {SRC_PATH}\n",
        "    ! rm -rf {KFP_COMPONENTS_PATH}\n",
        "    ! rm -rf {TRAIN_PIPELINES_PATH}\n",
        "    ! rm -rf {TEST_PIPELINES_PATH}"
@@ -1,870 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "view-in-github"
-      },
-      "source": [
-        "<a href=\"https://colab.research.google.com/github/Narwhalprime/vertex-ai-samples/blob/main/notebooks/community/pipelines/google_cloud_pipeline_components_cloud_natural_language_pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "1142fd18"
-      },
-      "outputs": [],
-      "source": [
-        "# Copyright 2022 Google LLC\n",
-        "#\n",
-        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BwO30Ag12YcB"
-      },
-      "source": [
-        "# Vertex Pipelines: Cloud Natural Language model training pipeline\n",
-        "<table align=\"left\">\n",
-        "\n",
-        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/natural_language/cloud_natural_language_pipeline.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
-        "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/natural_language/cloud_natural_language_pipeline.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
-        "    </a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/ai/platform/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/natural_language/cloud_natural_language_pipeline.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>\n",
-        "</table>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "tvgnzT1CKxrO"
-      },
-      "source": [
-        "## Overview\n",
-        "This notebook shows how to use [Google Cloud Pipeline Components SDK](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction) and additional components in this directory to run a machine learning pipeline in [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) to train a TensorFlow text classification model.\n",
-        "\n",
-        "In this pipeline, the model training Docker image utilizes [TFHub](https://tfhub.dev/) models to perform state-of-the-art text classification training. The image is pre-built and ready to use, so no additional Docker setup is required."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d975e698c9a4"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "In this tutorial, you learn how to construct an end-to-end training pipeine within Vertex AI pipelines that ingests a dataset, trains a text classification model on it, and outputs evaluation metrics.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
-        "\n",
-        "- Vertex AI Pipelines\n",
-        "- Vertex AI Datasets\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- Define Kubeflow pipeline components\n",
-        "- Setup Kubeflow pipeline\n",
-        "- Run pipeline on Vertex AI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "08d289fa873f"
-      },
-      "source": [
-        "## Dataset\n",
-        "\n",
-        "This notebook requires that the user has two datasets exported from Vertex AI [managed datasets](https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets): one with train and validation data splits, and the other with test data used for evaluation. Please ensure no data is shared between the two datasets (in particular, no evaluation data should be part of the train or validation splits). To export a Vertex AI dataset, please follow the following public docs:\n",
-        "* [Preparing data](https://cloud.google.com/vertex-ai/docs/text-data/classification/prepare-data)\n",
-        "* [Creating a Vertex AI dataset](https://cloud.google.com/vertex-ai/docs/text-data/classification/create-dataset) from the above data\n",
-        "* [Exporting dataset and its annotations](https://cloud.google.com/vertex-ai/docs/datasets/export-metadata-annotations); ensure the resulting export is located in a Google Cloud Storage (GCS) bucket you own. You may need to manually separate the test split data into its own file."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "aed92deeb4a0"
-      },
-      "source": [
-        "## Costs\n",
-        "\n",
-        "This tutorial uses billable components of Google Cloud:\n",
-        "\n",
-        "* Vertex AI\n",
-        "* Cloud Storage\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": {
-        "id": "setup_local"
-      },
-      "source": [
-        "## Setup\n",
-        "\n",
-        "If you are using Colab or Google Vertex AI Workbench Notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
-        "\n",
-        "***NOTE***: This notebook has been tested in the following environment:\n",
-        "\n",
-        "* Python version = 3.8\n",
-        "\n",
-        "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
-        "\n",
-        "- The Cloud Storage SDK\n",
-        "- Python 3\n",
-        "- virtualenv\n",
-        "- Jupyter notebook running in a virtual environment with Python 3\n",
-        "\n",
-        "The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n",
-        "\n",
-        "1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
-        "\n",
-        "2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
-        "\n",
-        "3. [Install virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
-        "\n",
-        "4. Activate that environment and run `pip3 install Jupyter` in a terminal shell to install Jupyter.\n",
-        "\n",
-        "5. Run `jupyter notebook` on the command line in a terminal shell to launch Jupyter.\n",
-        "\n",
-        "6. Open this notebook in the Jupyter Notebook Dashboard.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "568d5c16"
-      },
-      "source": [
-        "### Install additional packages\n",
-        "\n",
-        "Run the following commands to setup the packages for this notebook. Note that the last code snippet in this section restarts your kernel in order to load the installs properly, so when initalizing this notebook from scratch, it is recommended to run up to that cell, then afterwards you may start running the cell after that."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dac98aac"
-      },
-      "outputs": [],
-      "source": [
-        "# Install using pip3\n",
-        "!pip3 install -U tensorflow google-cloud-pipeline-components google-cloud-aiplatform kfp==1.8.16 \"shapely<2\" -q"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "alRWYgYTdz7P"
-      },
-      "outputs": [],
-      "source": [
-        "# Version check\n",
-        "# This has been tested with KFP 1.8.16\n",
-        "! python3 -c \"import kfp; print('KFP SDK version: {}'.format(kfp.__version__))\"\n",
-        "! python3 -c \"import google_cloud_pipeline_components; print('google_cloud_pipeline_components version: {}'.format(google_cloud_pipeline_components.__version__))\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "d0a15440"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    # Automatically restart kernel after installs\n",
-        "    import IPython\n",
-        "\n",
-        "    app = IPython.Application.instance()\n",
-        "    app.kernel.do_shutdown(True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "B9IYalYObAbY"
-      },
-      "source": [
-        "## Before you begin\n",
-        "\n",
-        "### Set up your Google Cloud project\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,storage.googleapis.com).\n",
-        "\n",
-        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "VA_kzAIIj2G_"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
-        "authenticated. Skip this step.\n",
-        "\n",
-        "**If you are using Colab**, run the cell below and follow the instructions\n",
-        "when prompted to authenticate your account via oAuth.\n",
-        "\n",
-        "**Otherwise**, follow these steps:\n",
-        "\n",
-        "1. In the Cloud Console, go to the [**Create service account key**\n",
-        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
-        "\n",
-        "2. Click **Create service account**.\n",
-        "\n",
-        "3. In the **Service account name** field, enter a name, and\n",
-        "   click **Create**.\n",
-        "\n",
-        "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
-        "into the filter box, and select\n",
-        "   **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
-        "\n",
-        "5. Click *Create*. A JSON file that contains your key downloads to your\n",
-        "local environment.\n",
-        "\n",
-        "6. Enter the path to your service account key as the\n",
-        "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PyQmSRbKA8r-"
-      },
-      "outputs": [],
-      "source": [
-        "# If you are running this notebook in Colab, run this cell and follow the\n",
-        "# instructions to authenticate your GCP account. This provides access to your\n",
-        "# Cloud Storage bucket and lets you submit training jobs and prediction\n",
-        "# requests.\n",
-        "\n",
-        "import os\n",
-        "import sys\n",
-        "\n",
-        "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
-        "    \"DL_ANACONDA_HOME\"\n",
-        "):\n",
-        "    if \"google.colab\" in sys.modules:\n",
-        "        from google.colab import auth as google_auth\n",
-        "\n",
-        "        google_auth.authenticate_user()\n",
-        "\n",
-        "    # If you are running this notebook locally, replace the string below with the\n",
-        "    # path to your service account key and run this cell to authenticate your GCP\n",
-        "    # account.\n",
-        "    elif not os.getenv(\"IS_TESTING\"):\n",
-        "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "set_service_account"
-      },
-      "source": [
-        "### Set project ID\n",
-        "\n",
-        "Set your project ID here. If you don't know this, the following snippet attempts to deterine this from your gcloud config. Please continue only if the notebook can see your desired project."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "AkqEd5Gin9mn"
-      },
-      "outputs": [],
-      "source": [
-        "PROJECT_ID = \"cloud-ml-language-test\"  # @param {type:\"string\"}\n",
-        "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
-        "    # Get your GCP project id from gcloud\n",
-        "    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
-        "    PROJECT_ID = shell_output[0]\n",
-        "print(\"Project ID:\", PROJECT_ID)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "OVO_gUqpFEP2"
-      },
-      "outputs": [],
-      "source": [
-        "!gcloud config set project $PROJECT_ID"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a27d4cee"
-      },
-      "source": [
-        "### Setup project information\n",
-        "\n",
-        "Enter information about your project and datasets here."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7e9477a2"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"us\"  # @param {type:\"string\"}\n",
-        "LOCATION = \"us-central1\"  # @param {type:\"string\"}\n",
-        "TRAINING_DATA_LOCATION = \"gs://dougchen-20221130-pipeline-colab-test/data-00001-of-00001.jsonl\"  # @param {type:\"string\"}\n",
-        "TASK_TYPE = \"CLASSIFICATION\"  # @param [\"CLASSIFICATION\", \"MULTILABEL_CLASSIFICATION\"]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "o-MZnHsimbOH"
-      },
-      "outputs": [],
-      "source": [
-        "# Since we are training a custom model, we need to specify the list of possible\n",
-        "# classes/labels.\n",
-        "# e.g, [\"FirstClass\", \"SecondClass\"]\n",
-        "# An additional class \"[UNK]\" will be added to the list indicating that none of\n",
-        "# the specified labels are a match.\n",
-        "CLASS_NAMES = [\"\"]\n",
-        "\n",
-        "# This is a list of GCS URIs; e.g., [\"gs://your-bucket-name-here/your-input-file.jsonl\"].\n",
-        "TEST_DATA_URIS = [\"gs://your-bucket-name-here/your-input-file.jsonl\"]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### UUID\n",
-        "\n",
-        "To avoid name collisions with other resources in your project, you can create a UUID with the code below and append it onto the name of the bucket(s) created in this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "wh9sgzemwLXE"
-      },
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "import string\n",
-        "\n",
-        "\n",
-        "# Generate a uuid of a specifed length(default=8)\n",
-        "def generate_uuid(length: int = 8) -> str:\n",
-        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
-        "\n",
-        "\n",
-        "UUID = generate_uuid()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bucket:mbsdk"
-      },
-      "source": [
-        "### Create a Cloud Storage bucket\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "When you initialize the Vertex AI SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
-        "\n",
-        "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bucket"
-      },
-      "outputs": [],
-      "source": [
-        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "autoset_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
-        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "create_bucket"
-      },
-      "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dO0NV93IwLXF"
-      },
-      "outputs": [],
-      "source": [
-        "!gsutil mb -l $REGION $BUCKET_URI"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "validate_bucket"
-      },
-      "source": [
-        "Finally, validate access to your Cloud Storage bucket by examining its contents:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Hg5f2oKBwLXG"
-      },
-      "outputs": [],
-      "source": [
-        "!gsutil ls -al $BUCKET_URI"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "EuFETRptyKXc"
-      },
-      "outputs": [],
-      "source": [
-        "from google.cloud import aiplatform\n",
-        "\n",
-        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f3a09765"
-      },
-      "source": [
-        "## Create training pipeline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "89bb4a50"
-      },
-      "source": [
-        "### Import libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0f361e65"
-      },
-      "outputs": [],
-      "source": [
-        "from google_cloud_pipeline_components.aiplatform import ModelBatchPredictOp\n",
-        "from google_cloud_pipeline_components.experimental import natural_language\n",
-        "from google_cloud_pipeline_components.experimental.evaluation import (\n",
-        "    GetVertexModelOp, ModelEvaluationClassificationOp,\n",
-        "    TargetFieldDataRemoverOp)\n",
-        "from kfp import components\n",
-        "from kfp.v2 import compiler, dsl"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "d33c87e4-2ada-4b87-bf75-064247f3162d"
-      },
-      "source": [
-        "### Define constants"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "36ceb9f8"
-      },
-      "outputs": [],
-      "source": [
-        "# Worker pool specs\n",
-        "TRAINING_MACHINE_TYPE = \"n1-highmem-8\"\n",
-        "ACCELERATOR_TYPE = \"NVIDIA_TESLA_T4\"\n",
-        "ACCELERATOR_COUNT = 1\n",
-        "EVAL_MACHINE_TYPE = \"n1-highmem-8\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zAaMJKrhAe5L"
-      },
-      "source": [
-        "## Define components\n",
-        "\n",
-        "This pipeline is composed from the following components:\n",
-        "\n",
-        "- **train-tfhub-model** - Trains a new Tensorflow model using TFHub layers from pre-built Docker image\n",
-        "- **upload-tensorflow-model-to-google-cloud-vertex-ai** - Uploads resulting model to Vertex AI model registry\n",
-        "- **get-vertex-model** - Gets model that has just been uploaded as an artifact in pipeline\n",
-        "- **convert-dataset-export-for-batch-predict** - Preprocessing component that takes the test dataset exported from Vertex datasets and converts it to a simpler compatible one that is readable from the batch predict component\n",
-        "- **target-field-data-remover** - Removes the target field (i.e., label) in the test dataset for the downstream batch predict component\n",
-        "- **model-batch-predict** - Performs a batch prediction job\n",
-        "- **model-evaluation-classification** - Calculates the evaluation metrics from the above batch predict job and exports the metrics artifact\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "DKe2iQNKgpKG"
-      },
-      "outputs": [],
-      "source": [
-        "# Load upload TF model component\n",
-        "upload_tensorflow_model_to_vertex_op = components.load_component_from_url(\n",
-        "    \"https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/workaround_for_buggy_KFPv2_compiler/component.yaml\"\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TEnh9Pcx6Xfi"
-      },
-      "source": [
-        "### Define the pipeline\n",
-        "\n",
-        "The pipeline performs the following steps:\n",
-        "- Trains new text classification model\n",
-        "- Uploads model to Vertex AI Model Registry\n",
-        "- Performs preprocessing steps on test dataset export: formats data for batch predcition, removes target field\n",
-        "- Performs batch prediction on preprocessed test data\n",
-        "- Evaluates performance of model based on batch prediction output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2a67cde8"
-      },
-      "outputs": [],
-      "source": [
-        "@dsl.pipeline(name=\"text-classification-model\")\n",
-        "def pipeline():\n",
-        "    train_task = natural_language.TrainTextClassificationOp()(\n",
-        "        project=PROJECT_ID,\n",
-        "        location=LOCATION,\n",
-        "        machine_type=TRAINING_MACHINE_TYPE,\n",
-        "        accelerator_type=ACCELERATOR_TYPE,\n",
-        "        accelerator_count=ACCELERATOR_COUNT,\n",
-        "        input_data_path=TRAINING_DATA_LOCATION,\n",
-        "        input_format=\"jsonl\",\n",
-        "        natural_language_task_type=TASK_TYPE,\n",
-        "    )\n",
-        "\n",
-        "    upload_task = upload_tensorflow_model_to_vertex_op(\n",
-        "        model=train_task.outputs[\"model_output\"]\n",
-        "    )\n",
-        "\n",
-        "    get_model_task = GetVertexModelOp(\n",
-        "        model_resource_name=upload_task.outputs[\"model_name\"]\n",
-        "    )\n",
-        "\n",
-        "    classification_type = (\n",
-        "        \"multilabel\" if TASK_TYPE == \"MULTILABEL_CLASSIFICATION\" else \"multiclass\"\n",
-        "    )\n",
-        "\n",
-        "    convert_dataset_task = natural_language.ConvertDatasetExportForBatchPredictOp(\n",
-        "        file_paths=TEST_DATA_URIS, classification_type=classification_type\n",
-        "    )\n",
-        "\n",
-        "    target_field_remover_task = TargetFieldDataRemoverOp(\n",
-        "        project=PROJECT_ID,\n",
-        "        location=LOCATION,\n",
-        "        root_dir=BUCKET_URI,\n",
-        "        gcs_source_uris=convert_dataset_task.outputs[\"output_files\"],\n",
-        "        target_field_name=\"labels\",\n",
-        "        instances_format=\"jsonl\",\n",
-        "    )\n",
-        "\n",
-        "    # Note: ModelBatchPredictOp doesn't support accelerators currently.\n",
-        "    batch_predict_task = ModelBatchPredictOp(\n",
-        "        project=PROJECT_ID,\n",
-        "        location=LOCATION,\n",
-        "        model=get_model_task.outputs[\"model\"],\n",
-        "        job_display_name=\"nl-batch-predict-evaluation\",\n",
-        "        gcs_source_uris=target_field_remover_task.outputs[\"gcs_output_directory\"],\n",
-        "        instances_format=\"jsonl\",\n",
-        "        predictions_format=\"jsonl\",\n",
-        "        gcs_destination_output_uri_prefix=BUCKET_URI,\n",
-        "        machine_type=EVAL_MACHINE_TYPE,\n",
-        "    )\n",
-        "\n",
-        "    # Note: Because we're running a custom training pipeline, the model source\n",
-        "    # is detected as Custom and thus it doesn't use AutoML NL's default settings\n",
-        "    # and fails if class_labels is excluded.\n",
-        "    ModelEvaluationClassificationOp(\n",
-        "        project=PROJECT_ID,\n",
-        "        location=LOCATION,\n",
-        "        root_dir=BUCKET_URI,\n",
-        "        class_labels=CLASS_NAMES + [\"[UNK]\"],\n",
-        "        predictions_gcs_source=batch_predict_task.outputs[\"gcs_output_directory\"],\n",
-        "        predictions_format=\"jsonl\",\n",
-        "        prediction_label_column=\"prediction.displayNames\",\n",
-        "        prediction_score_column=\"prediction.confidences\",\n",
-        "        ground_truth_gcs_source=convert_dataset_task.outputs[\"output_files\"],\n",
-        "        ground_truth_format=\"jsonl\",\n",
-        "        target_field_name=\"labels\",\n",
-        "        classification_type=TASK_TYPE,\n",
-        "    )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "3211ba19"
-      },
-      "source": [
-        "### Compile the pipeline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "c368c73f"
-      },
-      "outputs": [],
-      "source": [
-        "compiler.Compiler().compile(pipeline, \"nl_pipeline.json\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "l_Vxwz5cdF5f"
-      },
-      "source": [
-        "Running the above line will generate a file locally or in Colab's directory."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ax0jOxIaholy"
-      },
-      "source": [
-        "### Run the pipeline\n",
-        "\n",
-        "This sends a create pipeline job request to Vertex Pipelines. Note that this  task run synchronously and may take a while to complete.\n",
-        "\n",
-        "You may view the progress of the job at any time by clicking on the generated links (after \"View Pipeline Job\" in the console output of the cell below). Once the pipeline finishes, you may examine the artifacts produced from this pipeline. See "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Wfs7QOSxhp_n"
-      },
-      "outputs": [],
-      "source": [
-        "job = aiplatform.PipelineJob(\n",
-        "    display_name=\"nl_pipeline\",\n",
-        "    template_path=\"nl_pipeline.json\",\n",
-        "    location=LOCATION,\n",
-        "    enable_caching=True,\n",
-        "    parameter_values={},\n",
-        ")\n",
-        "\n",
-        "job.run()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UIyGPaihWJWn"
-      },
-      "source": [
-        "Once the pipeline successfully finishes, go to the pipeline and examine the resulting metrics artifacts for the results. Otherwise, refer to the failing step(s) in the pipeline to determine the cause of any errors."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "OoexTJTy9jnH"
-      },
-      "source": [
-        "## View model evaluation results\n",
-        "\n",
-        "To check the results of evaluation after pipeline execution, find the \"model-evaluation-classification\" subdirectory in the Cloud Storage bucket created by this pipeline. You may also run the following to directly output the contents of the metrics file:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "h9EqPCQF9lN9"
-      },
-      "outputs": [],
-      "source": [
-        "import tensorflow as tf\n",
-        "\n",
-        "EVAL_TASK_NAME = \"model-evaluation-classification\"\n",
-        "PROJECT_NUMBER = job.gca_resource.name.split(\"/\")[1]\n",
-        "for _ in range(len(job.gca_resource.job_detail.task_details)):\n",
-        "    TASK_ID = job.gca_resource.job_detail.task_details[_].task_id\n",
-        "    EVAL_METRICS = (\n",
-        "        BUCKET_URI\n",
-        "        + \"/\"\n",
-        "        + PROJECT_NUMBER\n",
-        "        + \"/\"\n",
-        "        + job.name\n",
-        "        + \"/\"\n",
-        "        + EVAL_TASK_NAME\n",
-        "        + \"_\"\n",
-        "        + str(TASK_ID)\n",
-        "        + \"/executor_output.json\"\n",
-        "    )\n",
-        "    if tf.io.gfile.exists(EVAL_METRICS):\n",
-        "        ! gsutil cat $EVAL_METRICS"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "TpV-iwP9qw9c"
-      },
-      "source": [
-        "## Cleaning up\n",
-        "\n",
-        "To clean up the resources used by this pipeline, run the command below:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "sx_vKniMq9ZX"
-      },
-      "outputs": [],
-      "source": [
-        "# Delete GCS bucket.\n",
-        "!gsutil -m rm -r {BUCKET_URI}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "UMuyzrnZLoUa"
-      },
-      "source": [
-        "# Next steps\n",
-        "\n",
-        "For an alternate approach, please check out the [\"ready-to-go\" text classification pipeline](https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/pipelines/google_cloud_pipeline_components_ready_to_go_text_classification_pipeline.ipynb). This pipeline exposes the model logic for further customization if needed, and adds an additional pipeline step to deploy the model to enable online predictions."
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "collapsed_sections": [
-        "d975e698c9a4",
-        "08d289fa873f",
-        "d33c87e4-2ada-4b87-bf75-064247f3162d",
-        "3211ba19",
-        "TpV-iwP9qw9c",
-        "UMuyzrnZLoUa"
-      ],
-      "name": "google_cloud_pipeline_components_cloud_natural_language_pipeline.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
@@ -112,7 +112,7 @@ def benchmark(

    results = []
    for qps in qps_list:
-        num_requests = int(max(qps * duration_sec, 10))
+        num_requests = max(qps * duration_sec, 10)
        requests_for_qps = list(
            itertools.islice(itertools.cycle(requests), num_requests)
        )
@@ -12,10 +12,6 @@
        --errors-codes: A list of error codes to report errors. Otherwise, all errors are reported.
        --errors-csv: Report errors in CSV format
        
-        # options for automatic fixing
-        --fix: Automatic fix
-        --fix-codes: A list of fix codes to fix. Otherwise, all fix codes are enabled.
-        
        # index generatation
        --repo: Generate index in markdown format
        --web: Generate index in HTML format
@@ -23,7 +19,6 @@
        --desc: Add description to index
        --steps: Add steps to index
        --uses: Add "resources" used to index
-        --linkback: Add linkback to index
        
    Format of CSV file for notebooks to review:
    
@@ -14,7 +14,7 @@ The steps performed include the following:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+&nbsp;&nbsp;&nbsp;Learn more about [Tabular classification](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).


 [Create, train, and deploy an AutoML text classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-text-classification.ipynb)
@@ -34,7 +34,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Classification for text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text classification](https://cloud.google.com/vertex-ai/docs/text-data/classification/train-model).


 [Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb)
@@ -54,7 +54,9 @@ The steps performed are:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [BQML ARIMA+ forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).
+
+&nbsp;&nbsp;&nbsp;Learn more about [BQML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview).


 [AutoML Tabular Workflow pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb)
@@ -69,26 +71,7 @@ The steps performed are:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for E2E AutoML](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl).
-
-
-[Get started with AutoML Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/get_started_automl_training.ipynb)
-
-```
-Learn how to use `AutoML` for training with `Vertex AI`.
-
-The steps performed include:
-
- Train an image model
- Export the image model as an edge model
- Train a tabular model
- Export the tabular model as a cloud model
- Train a text model
- Train a video model
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI for AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular Workflows](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl).


 [AutoML training hierarchical forecasting for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_forecasting_hierarchical_batch.ipynb)
@@ -107,7 +90,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Hierarchical forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/hierarchical).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).


 [AutoML training image object detection model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb)
@@ -124,7 +107,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Image](https://cloud.google.com/vertex-ai/docs/image-data/object-detection/train-model).


 [AutoML tabular forecasting model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
@@ -141,7 +124,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/tutorials-samples).


 [AutoML training tabular regression model for batch prediction using BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb)
@@ -160,7 +143,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data).


 [AutoML training tabular regression model for online prediction using BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb)
@@ -179,7 +162,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data).


 [AutoML training text entity extraction model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb)
@@ -198,7 +181,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/train-model).


 [Training an AutoML text sentiment analysis model for online predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb)
@@ -218,7 +201,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/train-model).


 [AutoML training video action recognition model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb)
@@ -235,7 +218,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Action recognition for video data](https://cloud.google.com/vertex-ai/docs/training-overview#action_recognition_for_videos).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/action-recognition/train-model).


 [AutoML training video classification model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb)
@@ -252,7 +235,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/train-model).


 [AutoML training video object tracking model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb)
@@ -269,5 +252,5 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/object-tracking/train-model).

@@ -70,7 +70,7 @@
        "\n",
        "This notebook walks you through the major phases of building and using an AutoML text classification model on [Vertex AI](https://cloud.google.com/vertex-ai/docs/). \n",
        "\n",
-        "Learn more about [Classification for text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text)."
+        "Learn more about [AutoML Text classification](https://cloud.google.com/vertex-ai/docs/text-data/classification/train-model)."
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "In this tutorial, you take on the role of a store planner who must determine how much inventory they will need to order for each of their products and stores for November 2019. You accomplish this by training forecasting models using historical sales data. You start with a baseline model using BigQuery ML (BQML) [ARIMA_PLUS](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series) and then compare it against a [Vertex AI Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview) model.\n",
        "\n",
-        "Learn more about [BQML ARIMA+ forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview)."
+        "Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview)  and [BQML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview)."
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "In this tutorial, you will use two Vertex AI Tabular Workflows pipelines to train AutoML models using different configurations. You will see how `get_automl_tabular_pipeline_and_parameters` gives you the ability to customize the default AutoML Tabular pipeline, and how `get_skip_architecture_search_pipeline_and_parameters` allows you to reduce the training time and cost for an AutoML model by using the tuning results from a previous pipeline run.\n",
        "\n",
-        "Learn more about [Tabular Workflow for E2E AutoML](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl)."
+        "Learn more about [AutoML Tabular Workflows](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl)."
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to create hierarchical forecasting models using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users)and do batch prediction. Specifically, you predict a fictional store's sales based on historical sales data.\n",
        "\n",
-        "Learn more about [Hierarchical forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/hierarchical)."
+        "Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview)."
      ]
    },
    {
@@ -65,7 +65,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK to create image object detection models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images)."
+        "Learn more about [AutoML Image](https://cloud.google.com/vertex-ai/docs/image-data/object-detection/train-model)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK to create tabular forecasting models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview)."
+        "Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/tutorials-samples)."
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to create tabular regression models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview)."
+        "Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to create tabular regression models and do online prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview)."
+        "Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to create text entity extraction models and do online prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text)."
+        "Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/train-model)."
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK to train and deploy an [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) text sentiment analysis model and get online predictions from it.\n",
        "\n",
-        "Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text)."
+        "Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/train-model)."
      ]
    },
    {
@@ -65,7 +65,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK to create video action recognition models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Action recognition for video data](https://cloud.google.com/vertex-ai/docs/training-overview#action_recognition_for_videos)."
+        "Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/action-recognition/train-model)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK to create video classification models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos)."
+        "Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/train-model)."
      ]
    },
    {
@@ -64,7 +64,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to create video object tracking models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
-        "Learn more about [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos)."
+        "Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/object-tracking/train-model)."
      ]
    },
    {
@@ -15,25 +15,5 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
-
-
-[Get started with BigQuery ML Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/get_started_with_bqml_training.ipynb)
-
-```
-Learn how to use `BigQueryML` for training with `Vertex AI`.
-
-The steps performed include:
-
- Create a local BigQuery table in your project
- Train a BigQuery ML model
- Evaluate the BigQuery ML model
- Export the BigQuery ML model as a cloud model
- Upload the exported model as a `Vertex AI Model` resource
- Hyperparameter tune a BigQuery ML model with `Vertex AI Vizier`
- Automatically register a BigQuery ML model to `Vertex AI Model Registry`
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction).

@@ -63,7 +63,7 @@
        "\n",
        "This notebook is aimed at data analysts and data scientists who have data in BigQuery, want to train a model using BigQuery ML, register the model to Vertex AI Model Registry, and deploy it to an endpoint for real-time prediction. \n",
        "\n",
-        "Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction)."
      ]
    },
    {
@@ -29,7 +29,7 @@
        "id": "title:generic,gcp"
      },
      "source": [
-        "# Get started with BigQuery datasets\n",
+        "# E2E ML on GCP: MLOps stage 1 : data management: get started with BigQuery datasets\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
@@ -61,7 +61,7 @@
        "## Overview\n",
        "\n",
        "\n",
-        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers data management: get started with BigQuery datasets.\n",
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with BigQuery datasets.\n",
        "\n",
        "Learn more about [BigQuery Datasets](https://cloud.google.com/bigquery/docs/datasets-intro)."
      ]
@@ -29,7 +29,7 @@
        "id": "title:generic,gcp"
      },
      "source": [
-        "# Get started with Vertex AI Data Labeling\n",
+        "# E2E ML on GCP: MLOps stage 1 : formalization: get started with Vertex AI Data Labeling\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
@@ -62,7 +62,7 @@
        "## Overview\n",
        "\n",
        "\n",
-        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers data management: get started with Vertex AI Data Labeling service.\n",
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with Vertex AI Data Labeling service.\n",
        "\n",
        "Learn more about [Vertex AI Data Labeling](https://cloud.google.com/vertex-ai/docs/datasets/data-labeling-job)."
      ]
@@ -55,36 +55,3 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).

-
-[Get started with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb)
-
-```
-Learn how to use `Vertex AI Experiments` when training with `Vertex AI`.
-
-The steps performed include:
-
- Local (notebook) Training
-    - Create an experiment
-    - Create a first run in the experiment
-    - Log parameters and metrics
-    - Create artifact lineage
-    - Visualize the experiment results
-    - Execute a second run
-    - Compare the two runs in the experiment
- Cloud (`Vertex AI`) Training
-    - Within the training script:
-        - Create an experiment
-        - Log parameters and metrics
-        - Create artifact lineage
-    - Create a `Vertex AI Training` custom job
-    - Execute the custom job
-    - Visualize the experiment results
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
@@ -1,212 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "copyright"
-      },
-      "outputs": [],
-      "source": [
-        "# Copyright 2022 Google LLC\n",
-        "#\n",
-        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
-        "# you may not use this file except in compliance with the License.\n",
-        "# You may obtain a copy of the License at\n",
-        "#\n",
-        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
-        "#\n",
-        "# Unless required by applicable law or agreed to in writing, software\n",
-        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
-        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
-        "# See the License for the specific language governing permissions and\n",
-        "# limitations under the License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ee9d87d8ec00"
-      },
-      "source": [
-        "This notebook was authored with assistance from [Ivan Nardini](https://github.com/inardini)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "title:generic,gcp"
-      },
-      "source": [
-        "# Get started with Vertex AI Experiments\n",
-        "\n",
-        "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
-        "      View on GitHub\n",
-        "    </a>\n",
-        "  </td>\n",
-        "    <td>\n",
-        "        <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb\">\n",
-        "        <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
-        "        </a>\n",
-        "  </td>\n",
-        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
-        "      Open in Vertex AI Workbench\n",
-        "    </a>\n",
-        "  </td>\n",
-        "</table>\n",
-        "<br/><br/><br/>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "overview:mlops"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "\n",
-        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers get started with Vertex AI Experiments.\n",
-        "\n",
-        "Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments), [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata) and [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "objective:mlops,stage2,get_started_vertex_experiments"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "In this tutorial, you learn how to use `Vertex AI Experiments` when training with `Vertex AI`.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `Vertex AI Experiments`\n",
-        "- `Vertex ML Metadata`\n",
-        "- `Vertex AI Training`\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- Local (notebook) Training\n",
-        "    - Create an experiment\n",
-        "    - Create a first run in the experiment\n",
-        "    - Log parameters and metrics\n",
-        "    - Create artifact lineage\n",
-        "    - Visualize the experiment results\n",
-        "    - Execute a second run\n",
-        "    - Compare the two runs in the experiment\n",
-        "- Cloud (`Vertex AI`) Training\n",
-        "    - Within the training script:\n",
-        "        - Create an experiment\n",
-        "        - Log parameters and metrics\n",
-        "        - Create artifact lineage\n",
-        "    - Create a `Vertex AI Training` custom job\n",
-        "    - Execute the custom job\n",
-        "    - Visualize the experiment results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "recommendation:mlops,stage2,logging"
-      },
-      "source": [
-        "### Recommendations\n",
-        "\n",
-        "When doing E2E MLOps on Google Cloud, the following are some of the best practices for logging data when experimenting or formally training a model.\n",
-        "\n",
-        "#### Python Logging\n",
-        "\n",
-        "Use Python's logging package when doing ad-hoc training locally.\n",
-        "\n",
-        "#### Cloud Logging\n",
-        "\n",
-        "Use `Google Cloud Logging` when doing training on the cloud.\n",
-        "\n",
-        "#### Experiments\n",
-        "\n",
-        "Use Vertex AI Experiments in conjunction with logging when performing experiments to compare results for different experiment configurations."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "313c25f2f514"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "This tutorial does not use a dataset. References to example datasets is for demonstration purposes."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bd73a4bd07ef"
-      },
-      "source": [
-        "### Costs\n",
-        "This tutorial uses billable components of Google Cloud:\n",
-        "\n",
-        "- Vertex AI\n",
-        "\n",
-        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "source": [
-        "## Installations\n",
-        "\n",
-        "Install the following packages for executing this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "# The Vertex AI Workbench Notebook product has specific requirements\n",
-        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\") and not os.getenv(\"VIRTUAL_ENV\")\n",
-        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
-        "    \"/opt/deeplearning/metadata/env_version\"\n",
-        ")\n",
-        "\n",
-        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
-        "USER_FLAG = \"\"\n",
-        "if IS_WORKBENCH_NOTEBOOK:\n",
-        "    USER_FLAG = \"--user\"\n",
-        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
-        "! pip3 install {USER_FLAG} --upgrade gsutil -q"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "get_started_with_vertex_experiments.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
@@ -15,128 +15,6 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Monitoring for batch predictions](https://cloud.google.com/vertex-ai/docs/model-monitoring/model-monitoring-batch-predictions).


-[Vertex AI Model Monitoring for AutoML tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_automl.ipynb)
-
-```
-Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for AutoML tabular models.
-
-The steps performed include:
-
- Train an `AutoML` model.
- Deploy the `Model` resource to the `Endpoint` resource.
- Configure the `Endpoint` resource for model monitoring.
- Generate synthetic prediction requests for skew.
- Wait for email alert notification.
- Generate synthetic prediction requests for drift.
- Wait for email alert notification.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring).
-
-
-[Vertex AI Model Monitoring for batch prediction in AutoML image models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_automl_image_batch.ipynb)
-
-```
-Learn how to use `Vertex AI Model Monitoring` with `Vertex AI Batch Prediction` with an AutoML image classification model to detect an out of distribution image.
-
-The steps performed include:
-
-1. Train an AutoML image classification model.
-2. Submit a batch prediction containing both in and out of distribution images.
-3. Use Model Monitoring to calculate anomaly score on each image.
-4. Identify the images in the batch prediction request that are out of distribution.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring).
-
-
-[Vertex AI Model Monitoring for custom tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_custom.ipynb)
-
-```
-Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for custom tabular models.
-
-The steps performed include:
-
- Download a pre-trained custom tabular model.
- Upload the pre-trained model as a `Model` resource.
- Deploy the `Model` resource to the `Endpoint` resource.
- Configure the `Endpoint` resource for model monitoring.
- Generate synthetic prediction requests for skew.
- Wait for email alert notification.
- Generate synthetic prediction requests for drift.
- Wait for email alert notification.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring).
-
-
-[Vertex AI Model Monitoring for custom tabular models with TensorFlow Serving container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_custom_tf_serving.ipynb)
-
-```
-Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for custom tabular models, using a custom deployment container.
-
-The steps performed include:
-
- Download a pre-trained custom tabular model.
- Upload the pre-trained model as a `Model` resource.
- Deploying the `Model` resource to an `Endpoint` resource with `TensorFlow Serving` serving binary.
- Configure the `Endpoint` resource for model monitoring.
- Generate synthetic prediction requests for skew.
- Wait for email alert notification.
- Generate synthetic prediction requests for drift.
- Wait for email alert notification.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring).
-
-
-[Vertex AI Model Monitoring for setup for tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_setup.ipynb)
-
-```
-Learn to setup the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests.
-
-The steps performed include:
-
- Download a pre-trained custom tabular model.
- Upload the pre-trained model as a `Model` resource.
- Deploy the `Model` resource to the `Endpoint` resource.
- Configure the `Endpoint` resource for model monitoring.
-    - Skew and drift detection for feature inputs.
-    - Skew and drift detection for feature attributions.
- Automatic generation of the `input schema` by sending 1000 prediction request.
- List, pause, resume and delete monitoring jobs.
- Restart monitoring job with predefined `input schema`.
- View logged monitored data.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring).
-
-
-[Vertex AI Model Monitoring for XGBoost models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/get_started_with_model_monitoring_xgboost.ipynb)
-
-```
-Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests for XGBoost models.
-
-The steps performed include:
-
- Download a pre-trained XGBoost model.
- Upload the pre-trained model as a `Model` resource.
- Deploy the `Model` resource to the `Endpoint` resource.
- Configure the `Endpoint` resource for model monitoring:
-  - drift detection only -- no access to training data.
-  - predefine the input schema to map feature alias names to the unnamed array input to the model.
- Generate synthetic prediction requests for drift.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Monitoring](https://cloud.google.com/vertex-ai/docs/model-monitoring).
-
-
 [Vertex AI Model Monitoring with Explainable AI Feature Attributions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb)

 ```
@@ -17,5 +17,5 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction).

@@ -285,24 +285,3 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).

-
-[BQML and AutoML - Experimenting with Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb)
-
-```
-Learn how to use `Vertex AI Predictions` for rapid prototyping a model.
-
-The steps performed include:
-
- Creating a BigQuery and Vertex AI training dataset.
- Training a BigQuery ML and AutoML model.
- Extracting evaluation metrics from the BigQueryML and AutoML models.
- Selecting the best trained model.
- Deploying the best trained model.
- Testing the deployed model infrastructure.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
-
-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component).
-
@@ -0,0 +1,21 @@
+
+[BQML and AutoML - Experimenting with Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb)
+
+```
+Learn how to use `Vertex AI Predictions` for rapid prototyping a model.
+
+The steps performed include:
+
+- Creating a BigQuery and Vertex AI training dataset.
+- Training a BigQuery ML and AutoML model.
+- Extracting evaluation metrics from the BigQueryML and AutoML models.
+- Selecting the best trained model.
+- Deploying the best trained model.
+- Testing the deployed model infrastructure.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users).
+
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction).
+
@@ -33,19 +33,19 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>  \n",
        "\n",
        "  <td>\n",
-        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\" target='_blank'>\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/pipelines/rapid_prototyping_bqml_automl.ipynb\" target='_blank'>\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/structured_data/rapid_prototyping_bqml_automl.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -66,7 +66,7 @@
        "\n",
        "<img src=\"https://storage.googleapis.com/rafacarv-public-bucket-do-not-delete/abalone/automl_and_bqml.png\" />\n",
        "\n",
-        "Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component) and [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component)."
+        "Learn more about [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction)."
      ]
    },
    {
@@ -12,7 +12,9 @@ The steps performed are:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).


 [Vertex AI TabNet](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb)
@@ -30,5 +32,7 @@ The steps performed are:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).

@@ -65,7 +65,7 @@
        "\n",
        "The goal of the tutorial is to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.\n",
        "\n",
-        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
+        "Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)."
      ]
    },
    {
@@ -67,7 +67,7 @@
        "\n",
        "TabNet uses a machine learning technique called sequential attention to select which model features to reason from at each step in the model. This mechanism makes it possible to explain how the model arrives at its predictions and helps it learn more accurate models. Thanks to this design, TabNet not only outperforms other neural networks and decision trees but also provides interpretable feature attributions. Releasing TabNet as a First Party Trainer in Vertex AI means you'll be able to easily take advantage of TabNet's architecture and explainability and use it to train models on your own data. \n",
        "\n",
-        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
+        "Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview)."
      ]
    },
    {
@@ -11,7 +11,9 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).


 [Wide & Deep Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb)
@@ -26,5 +28,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [Tabular Workflow for Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep).
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).

@@ -63,7 +63,7 @@
        "\n",
        "This notebook showcases how to run the TabNet algorithm using Vertex AI Tabular Workflows.\n",
        "\n",
-        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
+        "Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
      ]
    },
    {
@@ -63,7 +63,7 @@
        "\n",
        "This notebook showcases how to run the Wide & Deep algorithm using Vertex AI Tabular Workflows.\n",
        "\n",
-        "Learn more about [Tabular Workflow for Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep)."
+        "Learn more about [Vertex AI Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep) and [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction)."
      ]
    },
    {
@@ -1,22 +1,4 @@

-[Get started with Vertex AI Distributed Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/get_started_with_vertex_distributed_training.ipynb)
-
-```
-Learn how to use `Vertex AI Distributed Training` for when training with `Vertex AI`.
-
-The steps performed include:
-
- `MirroredStrategy`: Train on a single VM with multiple GPUs.
- `MultiWorkerMirroredStrategy`: Train on multiple VMs with automatic setup of replicas.
- `MultiWorkerMirroredStrategy`: Train on multiple VMs with fine grain control of replicas.
- `ReductionServer`: Train on multiple VMS and sync updates across VMS with `Vertex AI Reduction Server`.
- `TPUTraining`: Train with multiple Cloud TPUs.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Distributed Training](https://cloud.google.com/vertex-ai/docs/training/distributed-training).
-
-
 [Run hyperparameter tuning for a TensorFlow model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/hyperparameter_tuning_tensorflow.ipynb)

 ```
@@ -83,7 +83,7 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery](https://cloud.google.com/bigquery).


 [Build a fraud detection model on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb)
@@ -127,7 +127,7 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex).


 [Inventory prediction on ecommerce data using Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/inventory-prediction/inventory_prediction.ipynb)
@@ -194,7 +194,7 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).

-&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex).


 [Sentiment Analysis using AutoML Natural Language and Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/sentiment_analysis/Sentiment_Analysis.ipynb)
@@ -63,7 +63,7 @@
        "\n",
        "This notebook is written for data analysts and data scientists who have data in BigQuery and want to perform exploratory data analysis to gather insights from that data in an interactive environment.\n",
        "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery](https://cloud.google.com/bigquery)."
      ]
    },
    {
@@ -92,7 +92,7 @@
        "\n",
        "This tutorial shows you how to train, evaluate a propensity model in BigQuery ML to predict user retention on a mobile game, based on app measurement data from Google Analytics 4.\n",
        "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
      ]
    },
    {
@@ -87,7 +87,7 @@
        "\n",
        "*Note: This notebook file was developed to run in a [Vertex AI Workbench managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/list/managed) instance using the Python (Local) kernel. Some components of this notebook may not work in other notebook environments.*\n",
        "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
      ]
    },
    {