migration: experiments

2026-09-26 22:51:56 +00:00 · 2023-01-19 22:19:39 +00:00 · 2023-01-19 20:55:00 +00:00 · 2023-01-19 20:45:54 +00:00 · 2023-01-19 20:43:39 +00:00 · 2023-01-19 20:33:38 +00:00
69 changed files with 23326 additions and 1983 deletions
@@ -11,4 +11,3 @@ google-cloud-storage
 google-cloud-build
 ratemate
 GitPython
-google-api-core==2.10
@@ -0,0 +1,62 @@
+# 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()
@@ -30,6 +30,7 @@
 /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
@@ -37,3 +38,6 @@
 /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
+
@@ -0,0 +1,3 @@
+# 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.
@@ -8,7 +8,7 @@
      },
      "outputs": [],
      "source": [
-        "# Copyright 2021 Google LLC\n",
+        "# Copyright 2023 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",
@@ -24,6 +24,7 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "JAPoU8Sm5E6e"
@@ -32,20 +33,28 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/matching_engine/matching_engine_for_indexing.ipynb\">\n",
-        "      Run in Google Cloud Notebooks\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/matching_engine/matching_engine_for_indexing.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\">\n",
+        "      Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/matching_engine/matching_engine_for_indexing.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/matching_engine/matching_engine_for_indexing.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/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/matching_engine/matching_engine_for_indexing.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>"
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "tvgnzT1CKxrO"
@@ -53,25 +62,49 @@
      "source": [
        "## Overview\n",
        "\n",
-        "This example demonstrates how to use the GCP ANN Service. It is a high scale, low latency solution, to find similar vectors (or more specifically \"embeddings\") for a large corpus. Moreover, it is a fully managed offering, further reducing operational overhead. It is built upon [Approximate Nearest Neighbor (ANN) technology](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) developed by Google Research.\n",
-        "\n",
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [GloVe dataset](https://nlp.stanford.edu/projects/glove/).\n",
-        "\n",
+        "This example demonstrates how to use Vertex AI Matching Engine. It is a high scale, low latency solution, to find similar vectors (or more specifically \"embeddings\") for a large corpus. Moreover, it is a fully managed offering, further reducing operational overhead. It is built upon [Approximate Nearest Neighbor (ANN) technology](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) developed by Google Research."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "56e5f9699c6c"
+      },
+      "source": [
        "### Objective\n",
        "\n",
        "In this notebook, you will learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index. \n",
        "\n",
        "The steps performed include:\n",
        "\n",
-        "* Create ANN Index and Brute Force Index\n",
+        "* Create a Vertex AI Matching Engine Index and Brute Force Index\n",
        "* Create an IndexEndpoint with VPC Network\n",
-        "* Deploy ANN Index and Brute Force Index\n",
-        "* Perform online query\n",
-        "* Compute recall\n",
-        "\n",
+        "* Deploy a Vertex AI Matching Engine Index and Brute Force Index\n",
+        "* Perform online queries\n",
+        "* Submit batch queries\n",
+        "* Compute recall metric"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0aaef374550b"
+      },
+      "source": [
+        "### Dataset\n",
        "\n",
+        "The dataset used for this tutorial is the [GloVe dataset](https://nlp.stanford.edu/projects/glove/)."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5e2eba58ad71"
+      },
+      "source": [
        "### Costs \n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
@@ -87,6 +120,7 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "S5zc4kbEiYCm"
@@ -94,79 +128,47 @@
      "source": [
        "## Before you begin\n",
        "\n",
-        "* **Prepare a VPC network**.  To reduce any network overhead that might lead to unnecessary increase in overhead latency, it is best to call the ANN endpoints from your VPC via a direct [VPC Peering](https://cloud.google.com/vertex-ai/docs/general/vpc-peering) connection. The following section describes how to setup a VPC Peering connection if you don't have one. This is a one-time initial setup task. You can also reuse existing VPC network and skip this section.\n",
-        "* **WARNING:** The match service gRPC API (to create online queries against your deployed index) has to be executed in a Google Cloud Notebook instance that is created with the following requirements:\n",
-        "  * **In the same region as where your ANN service is deployed** (for example, if you set `REGION = \"us-central1\"` as same as the tutorial, the notebook instance has to be in `us-central1`).\n",
-        "  * **Make sure you select the VPC network you created for ANN service** (instead of using the \"default\" one). That is, you will have to create the VPC network below and then create a new notebook instance that uses that VPC.  \n",
-        "  * If you run it in the colab or a Google Cloud Notebook instance in a different VPC network or region, the gRPC API will fail to peer the network (InactiveRPCError)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "lW2LneA5mmmP"
-      },
-      "outputs": [],
-      "source": [
-        "PROJECT_ID = \"<your_project_id>\"  # @param {type:\"string\"}\n",
-        "NETWORK_NAME = \"ucaip-haystack-vpc-network\"  # @param {type:\"string\"}\n",
-        "PEERING_RANGE_NAME = \"ucaip-haystack-range\"\n",
+        "### Set up your Google Cloud project\n",
        "\n",
-        "# Create a VPC network\n",
-        "! gcloud compute networks create {NETWORK_NAME} --bgp-routing-mode=regional --subnet-mode=auto --project={PROJECT_ID}\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
        "\n",
-        "# Add necessary firewall rules\n",
-        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-icmp --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow icmp\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 $300 free credit towards your compute/storage costs.\n",
        "\n",
-        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-internal --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow all --source-ranges 10.128.0.0/9\n",
+        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
        "\n",
-        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-rdp --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow tcp:3389\n",
+        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
        "\n",
-        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-ssh --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow tcp:22\n",
-        "\n",
-        "# Reserve IP range\n",
-        "! gcloud compute addresses create {PEERING_RANGE_NAME} --global --prefix-length=16 --network={NETWORK_NAME} --purpose=VPC_PEERING --project={PROJECT_ID} --description=\"peering range for uCAIP Haystack.\"\n",
-        "\n",
-        "# Set up peering with service networking\n",
-        "! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={NETWORK_NAME} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}"
+        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
-        "id": "d3uj8x73nDX_"
-      },
-      "source": [
-        "* Authentication: `$ gcloud auth login` rerun this in Google Cloud Notebook terminal when you are logged out and need the credential again."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "i7EUnXsZhAGF"
+        "id": "4700b0e39c5d"
      },
      "source": [
        "### Installation\n",
        "\n",
-        "Download and install the latest (preview) version of the Vertex SDK for Python."
+        "Download and install the latest version of the Vertex AI SDK for Python."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "wyy5Lbnzg5fi"
+        "id": "014470c6a8de"
      },
      "outputs": [],
      "source": [
-        "! pip install -U git+https://github.com/googleapis/python-aiplatform.git@main-test --user"
+        "! pip install -U git+https://github.com/googleapis/python-aiplatform.git@main --user"
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
-        "id": "irSMQn6gZ19l"
+        "id": "cf00462144f7"
      },
      "source": [
        "Install the `h5py` to prepare sample dataset, and the `grpcio-tools` for querying against the index. "
@@ -176,11 +178,15 @@
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "-h5sqwOEZ5Yq"
+        "id": "3f3e45e5a1d1"
      },
      "outputs": [],
      "source": [
-        "! pip install -U grpcio-tools --user\n",
+        "! pip install protobuf==3.20.*\n",
+        "! pip install -U google-api-python-client==1.8.0 --user\n",
+        "! pip install -U grpcio-tools==1.47.0 --user\n",
+        "! pip install -U grpcio==1.47.0 --user\n",
+        "! pip install -U grpcio-status==1.47.0 --user\n",
        "! pip install -U h5py --user"
      ]
    },
@@ -199,7 +205,7 @@
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "EzrelQZ22IZj"
+        "id": "aa1d87bdc90b"
      },
      "outputs": [],
      "source": [
@@ -215,79 +221,216 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
-        "id": "BF1j6f9HApxa"
+        "id": "249da91c1011"
      },
      "source": [
-        "### Set up your Google Cloud project\n",
+        "### Set your project ID\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).\n",
-        "\n",
-        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "1. [Enable the Vertex AI API and Compute Engine API, and Service Networking API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,servicenetworking.googleapis.com).\n",
-        "\n",
-        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
-        "Cloud SDK uses the right project for all the commands in this notebook.\n",
-        "\n",
-        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "WReHDGG5g0XY"
-      },
-      "source": [
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
+        "**If you don't know your project ID**, try the following:\n",
+        "* Run `gcloud config list`.\n",
+        "* Run `gcloud projects list`.\n",
+        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "oM1iC_MfAts1"
+        "id": "10e0d2ee8c45"
      },
      "outputs": [],
      "source": [
-        "import os\n",
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
        "\n",
-        "PROJECT_ID = \"\"\n",
-        "\n",
-        "# Get your Google Cloud project ID from gcloud\n",
-        "if not os.getenv(\"IS_TESTING\"):\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)"
+        "# Set the project id\n",
+        "! gcloud config set project {PROJECT_ID}"
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
-        "id": "qJYoRfYng0XZ"
+        "id": "3fbfae3ff12a"
      },
      "source": [
-        "Otherwise, set your project ID here."
+        "### Set the region\n",
+        "\n",
+        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations).\n",
+        "* **WARNING:** \n",
+        "    *  **Make sure to [choose a region where Vertex AI services are available](https://cloud.google.com/vertex-ai/docs/general/locations#available_regions).**\n",
+        "    *  **If you use Vertex Workbench, the Notebook instance needs to be in the same region where your Vertex AI Matching Engine is deployed.** (for example, if you set `REGION = \"us-central1\"` as same as the tutorial, the notebook instance has to be in `us-central1`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "riG_qUokg0XZ"
+        "id": "71c3fd82024e"
      },
      "outputs": [],
      "source": [
-        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
-        "    PROJECT_ID = \"<your_project_id>\"  # @param {type:\"string\"}"
+        "REGION = \"us-central1\"  # @param {type: \"string\"}\n",
+        "\n",
+        "# Set the regions\n",
+        "! gcloud config set ai_platform/region {REGION}"
      ]
    },
    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "60c5a0f69ad8"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d118c95af93f"
+      },
+      "source": [
+        "**1. Vertex AI Workbench**\n",
+        "* Do nothing as you are already authenticated."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3035286fcdda"
+      },
+      "source": [
+        "**2. Local JupyterLab instance, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "455882ec0f11"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5097f3233d53"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2b88e46ac2c8"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fcdbb8929927"
+      },
+      "source": [
+        "**4. Service account or other**\n",
+        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7c6eef70dfdb"
+      },
+      "source": [
+        "### Prepare a VPC network\n",
+        "\n",
+        "To reduce any network overhead that might lead to unnecessary increase in overhead latency, it is best to call the Vertex AI Matching Engine endpoints from your VPC via a direct [VPC Peering](https://cloud.google.com/vertex-ai/docs/general/vpc-peering) connection. The following section describes how to setup a VPC Peering connection if you don't have one. This is a one-time initial setup task. You can also reuse existing VPC network and skip this section.\n",
+        "\n",
+        "* **WARNING:** The match service gRPC API (to create online queries against your deployed index) has to be executed in a Google Cloud Notebook instance that is created with the following requirements:\n",
+        "    * **Make sure you select the VPC network you created for Vertex AI Matching Engine service** (instead of using the \"default\" one). That is, you will have to create the VPC network below and then create a new notebook instance that uses that VPC.  \n",
+        "    * If you run it in the colab or a Google Cloud Notebook instance in a different VPC network or region, the gRPC API will fail to peer the network (InactiveRPCError)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ab38a8cc634c"
+      },
+      "outputs": [],
+      "source": [
+        "NETWORK_NAME = \"ucaip-haystack-vpc-network\"  # @param {type:\"string\"}\n",
+        "PEERING_RANGE_NAME = \"ucaip-haystack-range\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ec6bf3199835"
+      },
+      "outputs": [],
+      "source": [
+        "# Create a VPC network\n",
+        "! gcloud compute networks create {NETWORK_NAME} --bgp-routing-mode=regional --subnet-mode=auto --project={PROJECT_ID}\n",
+        "\n",
+        "# Add necessary firewall rules\n",
+        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-icmp --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow icmp\n",
+        "\n",
+        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-internal --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow all --source-ranges 10.128.0.0/9\n",
+        "\n",
+        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-rdp --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow tcp:3389\n",
+        "\n",
+        "! gcloud compute firewall-rules create {NETWORK_NAME}-allow-ssh --network {NETWORK_NAME} --priority 65534 --project {PROJECT_ID} --allow tcp:22\n",
+        "\n",
+        "# Reserve IP range\n",
+        "! gcloud compute addresses create {PEERING_RANGE_NAME} --global --prefix-length=16 --network={NETWORK_NAME} --purpose=VPC_PEERING --project={PROJECT_ID} --description=\"peering range for uCAIP Haystack.\""
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ddbace09fe81"
+      },
+      "source": [
+        "Create the VPC Peering. If you are running this from Vertex AI Workbench it is possible you might need your notebook's instance service or user account to have the Service Networking Admin Role"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "d329aa3c54d3"
+      },
+      "outputs": [],
+      "source": [
+        "# Set up peering with service networking\n",
+        "! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={NETWORK_NAME} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}"
+      ]
+    },
+    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "zgPO1eR3CYjk"
@@ -297,13 +440,11 @@
        "\n",
        "**The following steps are required, regardless of your notebook environment.**\n",
        "\n",
-        "Set the name of your Cloud Storage bucket below. It must be unique across all\n",
+        "Create a storage bucket to store intermediate artifacts such as datasets. Set the name of your Cloud Storage bucket below. It must be unique across all\n",
        "Cloud Storage buckets.\n",
        "\n",
-        "You may also change the `REGION` variable, which is used for operations\n",
-        "throughout the rest of this notebook. Make sure to [choose a region where Vertex AI services are\n",
-        "available](https://cloud.google.com/vertex-ai/docs/general/locations#available_regions). You may\n",
-        "not use a Multi-Regional Storage bucket for training with Vertex AI."
+        "* **WARNING:** \n",
+        "    * **You may not use a Multi-Regional Storage bucket for training with Vertex AI.**"
      ]
    },
    {
@@ -314,8 +455,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "REGION = \"us-central1\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"gs://[your-bucket-name-unique]\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -328,10 +468,14 @@
      "source": [
        "from datetime import datetime\n",
        "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
+        "UUID = datetime.now().strftime(\"%Y%m%d%H%M%S\")\n",
        "\n",
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "if (\n",
+        "    BUCKET_NAME == \"\"\n",
+        "    or BUCKET_NAME is None\n",
+        "    or BUCKET_NAME == \"gs://[your-bucket-name-unique]\"\n",
+        "):\n",
+        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + UUID"
      ]
    },
    {
@@ -351,7 +495,7 @@
      },
      "outputs": [],
      "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_NAME"
      ]
    },
    {
@@ -416,10 +560,7 @@
      },
      "outputs": [],
      "source": [
-        "REGION = \"us-central1\"\n",
        "ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n",
-        "NETWORK_NAME = \"ucaip-haystack-vpc-network\"  # @param {type:\"string\"}\n",
-        "\n",
        "\n",
        "AUTH_TOKEN = !gcloud auth print-access-token\n",
        "PROJECT_NUMBER = !gcloud projects list --filter=\"PROJECT_ID:'{PROJECT_ID}'\" --format='value(PROJECT_NUMBER)'\n",
@@ -429,10 +570,7 @@
        "\n",
        "print(\"ENDPOINT: {}\".format(ENDPOINT))\n",
        "print(\"PROJECT_ID: {}\".format(PROJECT_ID))\n",
-        "print(\"REGION: {}\".format(REGION))\n",
-        "\n",
-        "!gcloud config set project {PROJECT_ID}\n",
-        "!gcloud config set ai_platform/region {REGION}"
+        "print(\"REGION: {}\".format(REGION))"
      ]
    },
    {
@@ -523,12 +661,13 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "QuVl8DrWG8NS"
      },
      "source": [
-        "Upload the training data to GCS."
+        "Upload the training data to Google Cloud Storage"
      ]
    },
    {
@@ -539,9 +678,9 @@
      },
      "outputs": [],
      "source": [
-        "# NOTE: Everything in this GCS DIR will be DELETED before uploading the data.\n",
+        "# NOTE: Everything in this Google Cloud Storage directory will be DELETED before uploading the data\n",
        "\n",
-        "! gsutil rm -rf {BUCKET_NAME}/*"
+        "! gsutil rm -raf {BUCKET_NAME}/** 2> /dev/null || true"
      ]
    },
    {
@@ -567,21 +706,23 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "mglUPwHpJH98"
      },
      "source": [
-        "## Create Indexes\n"
+        "## Create the indexes\n"
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "qhIBCQ7dDSbW"
      },
      "source": [
-        "### Create ANN Index (for Production Usage)"
+        "### Create Vertex AI Matching Engine index (for production usage)"
      ]
    },
    {
@@ -597,6 +738,16 @@
        ")"
      ]
    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "14e1ed031d66"
+      },
+      "source": [
+        "Set constants"
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -611,14 +762,15 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "svLYiDf0OD2G"
      },
      "source": [
-        "Create the ANN index configuration:\n",
+        "#### Create the Vertex AI Matching Engine index configuration\n",
        "\n",
-        "Please read the documentation to understand the various configuration parameters that can be used to tune the index\n"
+        "Please read the [documentation](https://cloud.google.com/vertex-ai/docs/matching-engine/configuring-indexes) to understand the various configuration parameters that can be used to tune the index"
      ]
    },
    {
@@ -656,9 +808,9 @@
        "    }\n",
        ")\n",
        "\n",
-        "ann_index = {\n",
+        "matching_engine_index = {\n",
        "    \"display_name\": DISPLAY_NAME,\n",
-        "    \"description\": \"Glove 100 ANN index\",\n",
+        "    \"description\": \"Glove 100 Vertex AI Matching Engine Index\",\n",
        "    \"metadata\": struct_pb2.Value(struct_value=metadata),\n",
        "}"
      ]
@@ -671,7 +823,9 @@
      },
      "outputs": [],
      "source": [
-        "ann_index = index_client.create_index(parent=PARENT, index=ann_index)"
+        "matching_engine_index = index_client.create_index(\n",
+        "    parent=PARENT, index=matching_engine_index\n",
+        ")"
      ]
    },
    {
@@ -686,7 +840,7 @@
        "# This will take ~45 min.\n",
        "\n",
        "while True:\n",
-        "    if ann_index.done():\n",
+        "    if matching_engine_index.done():\n",
        "        break\n",
        "    print(\"Poll the operation to create index...\")\n",
        "    time.sleep(60)"
@@ -700,17 +854,18 @@
      },
      "outputs": [],
      "source": [
-        "INDEX_RESOURCE_NAME = ann_index.result().name\n",
+        "INDEX_RESOURCE_NAME = matching_engine_index.result().name\n",
        "INDEX_RESOURCE_NAME"
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "kSsqZuyoA1SG"
      },
      "source": [
-        "### Create Brute Force Index (for Ground Truth)\n",
+        "### Create brute force index (for ground truth)\n",
        "\n",
        "The brute force index uses a naive brute force method to find the nearest neighbors. This method is not fast or efficient. Hence brute force indices are not recommended for production usage. They are to be used to find the \"ground truth\" set of neighbors, so that the \"ground truth\" set can be used to measure recall of the indices being tuned for production usage. To ensure an apples to apples comparison, the `distanceMeasureType` and `featureNormType`, `dimensions` of the brute force index should match those of the production indices being tuned.\n",
        "\n",
@@ -725,8 +880,6 @@
      },
      "outputs": [],
      "source": [
-        "from google.protobuf import *\n",
-        "\n",
        "algorithmConfig = struct_pb2.Struct(\n",
        "    fields={\"bruteForceConfig\": struct_pb2.Value(struct_value=struct_pb2.Struct())}\n",
        ")\n",
@@ -796,12 +949,13 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "mglUPwHpJH98"
      },
      "source": [
-        "## Update Indexes\n",
+        "## Update the indexes\n",
        "\n",
        "Create incremental data file.\n"
      ]
@@ -863,10 +1017,10 @@
        "    }\n",
        ")\n",
        "\n",
-        "ann_index = {\n",
+        "matching_engine_index = {\n",
        "    \"name\": INDEX_RESOURCE_NAME,\n",
        "    \"display_name\": DISPLAY_NAME,\n",
-        "    \"description\": \"Glove 100 ANN index\",\n",
+        "    \"description\": \"Glove 100 Vertex AI Matching Engine Index\",\n",
        "    \"metadata\": struct_pb2.Value(struct_value=metadata),\n",
        "}"
      ]
@@ -879,7 +1033,7 @@
      },
      "outputs": [],
      "source": [
-        "ann_index = index_client.update_index(index=ann_index)"
+        "matching_engine_index = index_client.update_index(index=matching_engine_index)"
      ]
    },
    {
@@ -894,7 +1048,7 @@
        "# This will take ~45 min.\n",
        "\n",
        "while True:\n",
-        "    if ann_index.done():\n",
+        "    if matching_engine_index.done():\n",
        "        break\n",
        "    print(\"Poll the operation to update index...\")\n",
        "    time.sleep(60)"
@@ -908,17 +1062,18 @@
      },
      "outputs": [],
      "source": [
-        "INDEX_RESOURCE_NAME = ann_index.result().name\n",
+        "INDEX_RESOURCE_NAME = matching_engine_index.result().name\n",
        "INDEX_RESOURCE_NAME"
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "qV2xjAnDDObD"
      },
      "source": [
-        "## Create an IndexEndpoint with VPC Network"
+        "## Create an index endpoint with VPC network"
      ]
    },
    {
@@ -997,21 +1152,23 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "np2cgVuuIe9k"
      },
      "source": [
-        "## Deploy Indexes"
+        "## Deploy the indexes"
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "8Ew1UgcIIiJG"
      },
      "source": [
-        "### Deploy ANN Index"
+        "### Deploy a Vertex AI Matching Engine index"
      ]
    },
    {
@@ -1022,7 +1179,7 @@
      },
      "outputs": [],
      "source": [
-        "DEPLOYED_INDEX_ID = \"ann_glove_deployed\""
+        "DEPLOYED_INDEX_ID = \"matching_engine_glove_deployed\""
      ]
    },
    {
@@ -1033,13 +1190,23 @@
      },
      "outputs": [],
      "source": [
-        "deploy_ann_index = {\n",
+        "deploy_matching_engine_index = {\n",
        "    \"id\": DEPLOYED_INDEX_ID,\n",
        "    \"display_name\": DEPLOYED_INDEX_ID,\n",
        "    \"index\": INDEX_RESOURCE_NAME,\n",
        "}"
      ]
    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cb6d956d7419"
+      },
+      "source": [
+        "If errors occur with the next command wait some minutes for the index endpoint to be created and retry."
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -1049,7 +1216,7 @@
      "outputs": [],
      "source": [
        "r = index_endpoint_client.deploy_index(\n",
-        "    index_endpoint=INDEX_ENDPOINT_NAME, deployed_index=deploy_ann_index\n",
+        "    index_endpoint=INDEX_ENDPOINT_NAME, deployed_index=deploy_matching_engine_index\n",
        ")"
      ]
    },
@@ -1082,12 +1249,13 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "RNZnXmO5AhDO"
      },
      "source": [
-        "### Deploy Brute Force Index"
+        "### Deploy brute force index"
      ]
    },
    {
@@ -1158,12 +1326,13 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "6LCGvBNvBd8D"
      },
      "source": [
-        "## Create Online Queries\n",
+        "## Create online queries\n",
        "\n",
        "After you built your indexes, you may query against the deployed index through the online querying gRPC API (Match service) within the virtual machine instances from the same region (for example 'us-central1' in this tutorial).  \n",
        "\n",
@@ -1178,7 +1347,15 @@
        "\n",
        "* Compile the protocal buffer (see below)\n",
        "* Obtain the index endpoint\n",
-        "* Use a code-generated stub to make the call, passing the parameter values"
+        "* Use a code-generated stub to make the call, passing the parameter values\n",
+        "\n",
+        "### Troubleshooting connectivity issues\n",
+        "\n",
+        "In case you have connectivity errors please perform the following:\n",
+        "\n",
+        "* Verify that the index endpoint, index, and VPC are all in the same Google Cloud project\n",
+        "* Verify that the index endpoint, index, and VPC are all in the same region and it is a valid (e.g. us-central1)\n",
+        "* Verify the Network does not have a firewall rule which denies all egress connections. Else, disable this rule or overwrite it with another rule that allows connection to the index endpoint IP"
      ]
    },
    {
@@ -1351,12 +1528,13 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "8wXTSgz1Bl0x"
      },
      "source": [
-        "Obtain the Private Endpoint: "
+        "Obtain the private endpoint: "
      ]
    },
    {
@@ -1521,12 +1699,13 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "_mNwdU9_B_Ez"
      },
      "source": [
-        "### Batch Query\n",
+        "## Submit a batch query\n",
        "\n",
        "You can run multiple queries in a single RPC call using the BatchMatch API:"
      ]
@@ -1764,18 +1943,20 @@
        "]\n",
        "\n",
        "batch_request = match_service_pb2.BatchMatchRequest()\n",
-        "batch_request_ann = match_service_pb2.BatchMatchRequest.BatchMatchRequestPerIndex()\n",
+        "batch_request_matching_engine = (\n",
+        "    match_service_pb2.BatchMatchRequest.BatchMatchRequestPerIndex()\n",
+        ")\n",
        "batch_request_brute_force = (\n",
        "    match_service_pb2.BatchMatchRequest.BatchMatchRequestPerIndex()\n",
        ")\n",
-        "batch_request_ann.deployed_index_id = DEPLOYED_INDEX_ID\n",
+        "batch_request_matching_engine.deployed_index_id = DEPLOYED_INDEX_ID\n",
        "batch_request_brute_force.deployed_index_id = DEPLOYED_BRUTE_FORCE_INDEX_ID\n",
        "for query in queries:\n",
-        "    batch_request_ann.requests.append(get_request(query, DEPLOYED_INDEX_ID))\n",
+        "    batch_request_matching_engine.requests.append(get_request(query, DEPLOYED_INDEX_ID))\n",
        "    batch_request_brute_force.requests.append(\n",
        "        get_request(query, DEPLOYED_BRUTE_FORCE_INDEX_ID)\n",
        "    )\n",
-        "batch_request.requests.append(batch_request_ann)\n",
+        "batch_request.requests.append(batch_request_matching_engine)\n",
        "batch_request.requests.append(batch_request_brute_force)\n",
        "\n",
        "response = stub.BatchMatch(batch_request)\n",
@@ -1783,14 +1964,15 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "_mNwdU9_B_Ez"
      },
      "source": [
-        "### Compute Recall\n",
+        "### Compute the recall metric\n",
        "\n",
-        "Use deployed brute force Index as the ground truth to calculate the recall of ANN Index:"
+        "Use the deployed brute force index as the ground truth to calculate the recall of the Vertex AI Matching Engine index:"
      ]
    },
    {
@@ -1835,6 +2017,7 @@
      ]
    },
    {
+      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "TpV-iwP9qw9c"
@@ -1844,7 +2027,18 @@
        "\n",
        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
-        "You can also manually delete resources that you created by running the following code."
+        "\n",
+        "Otherwise, you can delete the individual resources you created in this tutorial:"
+      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "390c331dc7d9"
+      },
+      "source": [
+        "### Delete the Vertex AI Matching Engine resources"
      ]
    },
    {
@@ -1869,6 +2063,31 @@
      "source": [
        "index_endpoint_client.delete_index_endpoint(name=INDEX_ENDPOINT_NAME)"
      ]
+    },
+    {
+      "attachments": {},
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ff14a85c85fb"
+      },
+      "source": [
+        "### Delete the Google Cloud Storage bucket"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "68d4781faac4"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "delete_bucket = False\n",
+        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "    ! gsutil -m rm -r $BUCKET_NAME"
+      ]
    }
  ],
  "metadata": {
@@ -1028,6 +1028,9 @@
        "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 {USER_FLAG} -q --no-warn-conflicts"
+        "! pip3 install --upgrade tensorflow google-cloud-bigquery google-cloud-aiplatform \"shapely<2\" {USER_FLAG} -q --no-warn-conflicts"
      ]
    },
    {
@@ -72,7 +72,7 @@
      "source": [
        "## Overview\n",
        "\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",
+        "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",
        "\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."
      ]
@@ -397,13 +397,12 @@
      "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 {SRC_PATH} {KFP_COMPONENTS_PATH} {TRAIN_PIPELINES_PATH} {TEST_PIPELINES_PATH}"
+        "! mkdir -m 777 -p {KFP_COMPONENTS_PATH} {TRAIN_PIPELINES_PATH} {TEST_PIPELINES_PATH}"
      ]
    },
    {
@@ -425,14 +424,12 @@
      },
      "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 = 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()"
+        "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\""
      ]
    },
    {
@@ -1478,7 +1475,6 @@
        "# 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}"
@@ -0,0 +1,870 @@
+{
+  "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 = max(qps * duration_sec, 10)
+        num_requests = int(max(qps * duration_sec, 10))
        requests_for_qps = list(
            itertools.islice(itertools.cycle(requests), num_requests)
        )
@@ -12,6 +12,10 @@
        --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
@@ -19,6 +23,7 @@
        --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:
    
@@ -185,7 +190,21 @@ def parse_dir(directory: str) -> int:
    """
    exit_code = 0
    
+    sorted_entries = []
    entries = os.scandir(directory)
+    for entry in entries:
+
+        inserted = False
+        for ix in range(len(sorted_entries)):
+            if entry.name < sorted_entries[ix].name:
+                sorted_entries.insert(ix, entry)
+                inserted = True
+                break
+        
+        if not inserted:
+            sorted_entries.append(entry)
+    
+    entries = sorted_entries
    for entry in entries:
        if entry.is_dir():
            if entry.name[0] == '.':
@@ -1,23 +1,4 @@

-[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)
-
-```
-In this tutorial, you create an AutoML hierarchical forecasting model and deploy it for batch prediction using the Vertex AI SDK for Python.
-
-The steps performed include:
-
- Create a Vertex AI `TimeSeriesDataset` resource.
- Train the model.
- View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).
-
-
 [AutoML Tabular training and prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-tabular-classification.ipynb)

 ```
@@ -33,7 +14,7 @@ The steps performed include the following:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data).
+&nbsp;&nbsp;&nbsp;Learn more about [Classification for tabular data](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)
@@ -53,127 +34,7 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text classification](https://cloud.google.com/vertex-ai/docs/text-data/classification/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)
-
-```
-Learn how to create an AutoML video classification model from a Python script, and then do a batch prediction using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/train-model).
-
-
-[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)
-
-```
-Learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/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)
-
-```
-Learn how to create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a `Vertex AI Dataset` resource.
- Train an `AutoML` tabular forecasting `Model` resource.
- Obtain the evaluation metrics for the `Model` resource.
- Make a batch prediction.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/tutorials-samples).
-
-
-[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)
-
-```
-In this tutorial, you create an AutoML image object detection model from a Python script, and then do a batch prediction using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Image](https://cloud.google.com/vertex-ai/docs/image-data/object-detection/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)
-
-```
-Learn how to create an AutoML video action recognition model from a Python script, and then do a batch prediction using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a `Vertex AI Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/action-recognition/train-model).
-
-[AutoML Tabular Workflow pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb)
-
-```
-Learn how to create two regression models using [Vertex AI Pipelines](https://cloud.
-
-The steps performed are:
-
- Create a training pipeline that reduces the search space from the default to save time.
- Create a training pipeline that reuses the architecture search results from the previous pipeline to save time.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular Workflows](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl).
-
-
-[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)
-
-```
-Learn how to create an AutoML text sentiment analysis model and deploy it for online predictions from a Python script using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a `Vertex AI Dataset` resource.
- Create a training job for the AutoML model on the dataset.
- View the model evaluation metrics.
- Deploy the `Vertex AI Model` resource to a serving `Vertex AI Endpoint`.
- Make a prediction request to the deployed model.
- Undeploy the model from endpoint.
- Perform clean up process.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/train-model).
+&nbsp;&nbsp;&nbsp;Learn more about [Classification for text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text).


 [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)
@@ -193,19 +54,51 @@ The steps performed are:

 ```

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


-[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)
+[AutoML Tabular Workflow pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb)

 ```
-Learn how to create an AutoML tabular regression model and deploy for online prediction from a Python script using the Vertex AI SDK.
+Learn how to create two regression models using [Vertex AI Pipelines](https://cloud.
+
+The steps performed are:
+
+- Create a training pipeline that reduces the search space from the default to save time.
+- Create a training pipeline that reuses the architecture search results from the previous pipeline to save time.
+
+```
+
+&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:

- Create a Vertex `Dataset` resource.
+- 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).
+
+
+[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)
+
+```
+In this tutorial, you create an AutoML hierarchical forecasting model and deploy it for batch prediction using the Vertex AI SDK for Python.
+
+The steps performed include:
+
+- Create a Vertex AI `TimeSeriesDataset` resource.
 - Train the model.
 - View the model evaluation.
 - Deploy the `Model` resource to a serving `Endpoint` resource.
@@ -214,13 +107,13 @@ The steps performed include:

 ```

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


-[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)
+[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)

 ```
-Learn how to create an AutoML video object tracking model from a Python script, and then do a batch prediction using the Vertex AI SDK.
+In this tutorial, you create an AutoML image object detection model from a Python script, and then do a batch prediction using the Vertex AI SDK.

 The steps performed include:

@@ -231,7 +124,24 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/object-tracking/train-model).
+&nbsp;&nbsp;&nbsp;Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images).
+
+
+[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)
+
+```
+Learn how to create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a `Vertex AI Dataset` resource.
+- Train an `AutoML` tabular forecasting `Model` resource.
+- Obtain the evaluation metrics for the `Model` resource.
+- Make a batch prediction.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).


 [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)
@@ -250,5 +160,114 @@ The steps performed include:

 ```

-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data).
+&nbsp;&nbsp;&nbsp;Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+
+
+[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)
+
+```
+Learn how to create an AutoML tabular regression model and deploy for online prediction from a Python script using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model`.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+
+
+[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)
+
+```
+Learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model`.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text).
+
+
+[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)
+
+```
+Learn how to create an AutoML text sentiment analysis model and deploy it for online predictions from a Python script using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a `Vertex AI Dataset` resource.
+- Create a training job for the AutoML model on the dataset.
+- View the model evaluation metrics.
+- Deploy the `Vertex AI Model` resource to a serving `Vertex AI Endpoint`.
+- Make a prediction request to the deployed model.
+- Undeploy the model from endpoint.
+- Perform clean up process.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).
+
+
+[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)
+
+```
+Learn how to create an AutoML video action recognition model from a Python script, and then do a batch prediction using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a `Vertex AI Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Make a batch prediction.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Action recognition for video data](https://cloud.google.com/vertex-ai/docs/training-overview#action_recognition_for_videos).
+
+
+[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)
+
+```
+Learn how to create an AutoML video classification model from a Python script, and then do a batch prediction using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Make a batch prediction.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos).
+
+
+[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)
+
+```
+Learn how to create an AutoML video object tracking model from a Python script, and then do a batch prediction using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Make a batch prediction.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos).

@@ -65,7 +65,7 @@
        "\n",
        "**Note**: you may incur charges for training, prediction, storage, or usage of other Google Cloud products in connection with testing this SDK.\n",
        "\n",
-        "Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data)."
+        "Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview)."
      ]
    },
    {
@@ -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 [AutoML Text classification](https://cloud.google.com/vertex-ai/docs/text-data/classification/train-model)."
+        "Learn more about [Classification for text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text)."
      ]
    },
    {
@@ -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 [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)."
+        "Learn more about [BQML ARIMA+ forecasting for tabular data](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 [AutoML Tabular Workflows](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl)."
+        "Learn more about [Tabular Workflow for E2E AutoML](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 [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview)."
+        "Learn more about [Hierarchical forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/hierarchical)."
      ]
    },
    {
@@ -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 [AutoML Image](https://cloud.google.com/vertex-ai/docs/image-data/object-detection/train-model)."
+        "Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images)."
      ]
    },
    {
@@ -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 [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/tutorials-samples)."
+        "Learn more about [Forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview)."
      ]
    },
    {
@@ -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 [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data)."
+        "Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview)."
      ]
    },
    {
@@ -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 [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data)."
+        "Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview)."
      ]
    },
    {
@@ -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 [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/train-model)."
+        "Learn more about [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text)."
      ]
    },
    {
@@ -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 [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/train-model)."
+        "Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text)."
      ]
    },
    {
@@ -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 [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/action-recognition/train-model)."
+        "Learn more about [Action recognition for video data](https://cloud.google.com/vertex-ai/docs/training-overview#action_recognition_for_videos)."
      ]
    },
    {
@@ -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 [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/train-model)."
+        "Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos)."
      ]
    },
    {
@@ -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 [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/object-tracking/train-model)."
+        "Learn more about [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos)."
      ]
    },
    {
@@ -15,5 +15,25 @@ The steps performed include:

 ```

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

@@ -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/bigquery-ml/docs/introduction)."
+        "Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
      ]
    },
    {
@@ -1,4 +1,24 @@

+[Deploying Iris-detection model using FastAPI and Vertex AI custom container serving](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/SDK_Custom_Container_Prediction.ipynb)
+
+```
+Learn how to create, deploy and serve a custom classification model on Vertex AI.
+
+The steps performed include:
+
+- Train a model that uses flower's measurements as input to predict the class of iris.
+- Save the model and its serialized pre-processor.
+- Build a FastAPI server to handle predictions and health checks.
+- Build a custom container with model artifacts.
+- Upload and deploy custom container to Vertex AI Endpoints.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).
+
+
 [Training and deploying a sales forecasting model using FBProphet and Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/SDK_FBProphet_Forecasting_Online.ipynb)

 ```
@@ -22,23 +42,23 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).


-[Custom training and batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)
+[Training a TensorFlow model on BigQuery data](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb)

 ```
-Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
+Learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then get a prediction from the deployed model by sending data.

 The steps performed include:

- Create a `Vertex AI` custom job for training a TensorFlow model.
- Upload the trained model artifacts as a `Model` resource.
- Make a batch prediction.
+- Create a Vertex AI custom `TrainingPipeline` for training a model.
+- Train a TensorFlow model.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model` resource.

 ```

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).

-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
-

 [Profile model training performance using Profiler](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom_training_tensorboard_profiler.ipynb)

@@ -57,23 +77,23 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard Profiler](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-profiler).


-[Training a TensorFlow model on BigQuery data](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb)
+[Custom training and batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)

 ```
-Learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then get a prediction from the deployed model by sending data.
+Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.

 The steps performed include:

- Create a Vertex AI custom `TrainingPipeline` for training a model.
- Train a TensorFlow model.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model` resource.
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- Upload the trained model artifacts as a `Model` resource.
+- Make a batch prediction.

 ```

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).

+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
+

 [Custom training and online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-online.ipynb)

@@ -95,23 +115,3 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).

-
-[Deploying Iris-detection model using FastAPI and Vertex AI custom container serving](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/SDK_Custom_Container_Prediction.ipynb)
-
-```
-Learn how to create, deploy and serve a custom classification model on Vertex AI.
-
-The steps performed include:
-
- Train a model that uses flower's measurements as input to predict the class of iris.
- Save the model and its serialized pre-processor.
- Build a FastAPI server to handle predictions and health checks.
- Build a custom container with model artifacts.
- Upload and deploy custom container to Vertex AI Endpoints.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).
-
@@ -29,7 +29,7 @@
        "id": "title:generic,gcp"
      },
      "source": [
-        "# E2E ML on GCP: MLOps stage 1 : data management: get started with BigQuery datasets\n",
+        "# 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 for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with BigQuery datasets.\n",
+        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers 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": [
-        "# E2E ML on GCP: MLOps stage 1 : formalization: get started with Vertex AI Data Labeling\n",
+        "# 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 for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with Vertex AI Data Labeling service.\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",
        "\n",
        "Learn more about [Vertex AI Data Labeling](https://cloud.google.com/vertex-ai/docs/datasets/data-labeling-job)."
      ]
@@ -1,39 +1,4 @@

-[Compare pipeline runs with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_pipeline_runs.ipynb)
-
-```
-Learn how to use `Vertex AI Experiments` to log a pipeline job and compare different pipeline jobs.
-
-The steps performed include:
-
-* Formalize a training component
-* Build a training pipeline
-* Run several Pipeline jobs and log their results
-* Compare different Pipeline jobs
-
-```
-
-&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 AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-
-[Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_local_trained_models.ipynb)
-
-```
-Learn how to use Vertex AI Experiments to compare and evaluate model experiments.
-
-The steps performed include:
-
- log the model parameters
- log the loss and metrics on every epoch to TensorBoard
- log the evaluation metrics
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
-
-
 [Build Vertex AI Experiment lineage for custom training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb)

 ```
@@ -55,3 +20,71 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata).

+
+[Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_local_trained_models.ipynb)
+
+```
+Learn how to use Vertex AI Experiments to compare and evaluate model experiments.
+
+The steps performed include:
+
+- log the model parameters
+- log the loss and metrics on every epoch to TensorBoard
+- log the evaluation metrics
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
+
+
+[Compare pipeline runs with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_pipeline_runs.ipynb)
+
+```
+Learn how to use `Vertex AI Experiments` to log a pipeline job and compare different pipeline jobs.
+
+The steps performed include:
+
+* Formalize a training component
+* Build a training pipeline
+* Run several Pipeline jobs and log their results
+* Compare different Pipeline jobs
+
+```
+
+&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 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).
+
@@ -0,0 +1,212 @@
+{
+  "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
+}
@@ -1,24 +1,4 @@

-[Custom training tabular regression model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)
-
-```
-Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
-
-The steps performed include:
-
- Create a `Vertex AI` custom job for training a TensorFlow model.
- View the model evaluation for the trained model.
- Set explanation parameters for when the model is deployed.
- Upload the trained model artifacts and explanations as a `Model` resource.
- Make a batch prediction with explanations.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
-
-
 [AutoML training tabular binary classification model for batch explanation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb)

 ```
@@ -60,6 +40,26 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).


+[Custom training image classification model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
+
+```
+Learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- View the model evaluation for the trained model.
+- Set explanation parameters for when the model is deployed.
+- Upload the trained model artifacts and explanation parameters as a `Model` resource.
+- Make a batch prediction with explanations.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
+
+
 [Custom training image classification model for online prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb)

 ```
@@ -83,6 +83,26 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).


+[Custom training tabular regression model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)
+
+```
+Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- View the model evaluation for the trained model.
+- Set explanation parameters for when the model is deployed.
+- Upload the trained model artifacts and explanations as a `Model` resource.
+- Make a batch prediction with explanations.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
+
+
 [Custom training tabular regression model for online prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb)

 ```
@@ -129,23 +149,3 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).

-
-[Custom training image classification model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
-
-```
-Learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
-
-The steps performed include:
-
- Create a `Vertex AI` custom job for training a TensorFlow model.
- View the model evaluation for the trained model.
- Set explanation parameters for when the model is deployed.
- Upload the trained model artifacts and explanation parameters as a `Model` resource.
- Make a batch prediction with explanations.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
-
@@ -1,22 +1,4 @@

-[Create Vertex AI Matching Engine index](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
-
-```
-Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index.
-
-The steps performed include:
-
-* Create ANN Index and Brute Force Index
-* Create an IndexEndpoint with VPC Network
-* Deploy ANN Index and Brute Force Index
-* Perform online query
-* Compute recall
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview).
-
-
 [Introduction to builtin Swivel embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/intro-swivel.ipynb)

 ```
@@ -36,6 +18,24 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview).


+[Create Vertex AI Matching Engine index](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
+
+```
+Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index.
+
+The steps performed include:
+
+* Create ANN Index and Brute Force Index
+* Create an IndexEndpoint with VPC Network
+* Deploy ANN Index and Brute Force Index
+* Perform online query
+* Compute recall
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview).
+
+
 [Introduction to builtin Two-Towers embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/two-tower-model-introduction.ipynb)

 ```
@@ -1,75 +1,12 @@

-[Custom Image Classification w/custom training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb)
+[AutoML Image Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb)

 ```
-Learn how to train a tensorflow image classification model using a custom container and Vertex AI training.
+Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.

 The steps performed include:

- *Package the training code into a python application.*
- *Containerize the training application using Cloud Build and Artifact Registry.*
- *Create a custom container training job in Vertex AI and run it.*
- *Evaluate the model generated from the training job.*
- *Create a model resource for the trained model in Vertex AI Model Registry.*
- *Run a Vertex AI batch prediction job.*
- *Deploy the model resource to a Vertex AI Endpoint.*
- *Run a online prediction job on the model resource.*
- *Clean up the resources created.*
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[AutoML Video Classificaton](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb)
-
-```
-Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.
-
-The steps performed include:
-
- Train an AutoML video classification model.
- Make a batch prediction.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/tutorials/video-classification-automl/training).
-
-
-[AutoML Tabular Binary Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb)
-
-```
-In this tutorial, you create an AutoML tabular binary classification model and deploy for online prediction from a Python script using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/start/automl-users#tables).
-
-
-[Custom XGBoost model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb)
-
-```
-Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
-
-The steps performed include:
-
- Create a `Vertex AI` custom job for training a scikit-learn model.
- Upload the trained model artifacts as a `Model` resource.
+- Train an AutoML image classification model.
 - Make a batch prediction.
 - Deploy model to a endpoint
 - Make a online prediction
@@ -78,7 +15,7 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).

-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Image](https://cloud.google.com/vertex-ai/docs/tutorials/image-recognition-automl/training).


 [Custom Scikit-Learn model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb)
@@ -101,45 +38,37 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).


-[AutoML Image Object Detection](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb)
+[Hyperparameter Tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb)

 ```
-Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.
+Learn to use `Vertex AI Hyperparameter` to create and tune a custom trained model.

 The steps performed include:

- Train an AutoML object detection model.
+- Create a `Vertex AI` hyperparameter tuning job for training a TensorFlow model.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
+[AutoML Video Classificaton](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb)
+
+```
+Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.
+
+The steps performed include:
+
+- Train an AutoML video classification model.
 - Make a batch prediction.
- Deploy model to a endpoint
- Make a online prediction

 ```

 &nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).

-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Image](https://cloud.google.com/vertex-ai/docs/tutorials/image-recognition-automl/training).
-
-
-[AutoML Text Entity Extraction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb)
-
-```
-The objective of this notebook is to build a AutoML Text Entity Extraction Model.
-
-The steps performed include the following:
-
-* Set your task name, and GCS prefix
-* Copy AutoML video demo train data for creating managed dataset
-* Create a dataset on Vertex AI.
-* Configure a training job
-* Launch a training job and create a model on Vertex AI
-* Copy AutoML Video Demo Prediction Data for creating batch prediction job
-* Perform batch prediction job on the model
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/prepare-data).
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/tutorials/video-classification-automl/training).


 [AutoML Video Object Tracking](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb)
@@ -159,65 +88,6 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/video-intelligence/automl/object-tracking/docs/index-object-tracking).


-[Hyperparameter Tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb)
-
-```
-Learn to use `Vertex AI Hyperparameter` to create and tune a custom trained model.
-
-The steps performed include:
-
- Create a `Vertex AI` hyperparameter tuning job for training a TensorFlow model.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[AutoML Text Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb)
-
-```
-The objective of this notebook is to build a AutoML Video Classification Model.
-
-The steps performed include the following:
-
-* Set your task name, and GCS prefix
-* Copy AutoML video demo train data for creating managed dataset
-* Create a dataset on Vertex AI.
-* Configure a training job
-* Launch a training job and create a model on Vertex AI
-* Copy AutoML Video Demo Prediction Data for creating batch prediction job
-* Perform batch prediction job on the model
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/classification/prepare-data).
-
-
-[AutoML Text Sentiment Analysis](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb)
-
-```
-The objective of this notebook is to build a AutoML Text Sentiment Analysis model.
-
-The steps performed include the following:
-
-* Copy AutoML video demo train data for creating managed dataset
-* Create a dataset on Vertex AI.
-* Configure a training job
-* Launch a training job and create a model on Vertex AI
-* Copy AutoML Video Demo Prediction Data for creating batch prediction job
-* Perform batch prediction job on the model
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/prepare-data).
-
-
 [Custom Image Classification w/pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb)

 ```
@@ -242,14 +112,59 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).


-[AutoML Image Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb)
+[Custom Image Classification w/custom training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb)
+
+```
+Learn how to train a tensorflow image classification model using a custom container and Vertex AI training.
+
+The steps performed include:
+
+- *Package the training code into a python application.*
+- *Containerize the training application using Cloud Build and Artifact Registry.*
+- *Create a custom container training job in Vertex AI and run it.*
+- *Evaluate the model generated from the training job.*
+- *Create a model resource for the trained model in Vertex AI Model Registry.*
+- *Run a Vertex AI batch prediction job.*
+- *Deploy the model resource to a Vertex AI Endpoint.*
+- *Run a online prediction job on the model resource.*
+- *Clean up the resources created.*
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
+[AutoML Tabular Binary Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb)
+
+```
+In this tutorial, you create an AutoML tabular binary classification model and deploy for online prediction from a Python script using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model`
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/start/automl-users#tables).
+
+
+[AutoML Image Object Detection](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb)

 ```
 Learn to use `AutoML` to train an image model and use `Vertex AI Prediction` and `Vertex AI Batch Prediction` to do online and batch predictions.

 The steps performed include:

- Train an AutoML image classification model.
+- Train an AutoML object detection model.
 - Make a batch prediction.
 - Deploy model to a endpoint
 - Make a online prediction
@@ -260,3 +175,88 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [AutoML Image](https://cloud.google.com/vertex-ai/docs/tutorials/image-recognition-automl/training).

+
+[AutoML Text Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb)
+
+```
+The objective of this notebook is to build a AutoML Video Classification Model.
+
+The steps performed include the following:
+
+* Set your task name, and GCS prefix
+* Copy AutoML video demo train data for creating managed dataset
+* Create a dataset on Vertex AI.
+* Configure a training job
+* Launch a training job and create a model on Vertex AI
+* Copy AutoML Video Demo Prediction Data for creating batch prediction job
+* Perform batch prediction job on the model
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/classification/prepare-data).
+
+
+[AutoML Text Entity Extraction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb)
+
+```
+The objective of this notebook is to build a AutoML Text Entity Extraction Model.
+
+The steps performed include the following:
+
+* Set your task name, and GCS prefix
+* Copy AutoML video demo train data for creating managed dataset
+* Create a dataset on Vertex AI.
+* Configure a training job
+* Launch a training job and create a model on Vertex AI
+* Copy AutoML Video Demo Prediction Data for creating batch prediction job
+* Perform batch prediction job on the model
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/prepare-data).
+
+
+[AutoML Text Sentiment Analysis](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb)
+
+```
+The objective of this notebook is to build a AutoML Text Sentiment Analysis model.
+
+The steps performed include the following:
+
+* Copy AutoML video demo train data for creating managed dataset
+* Create a dataset on Vertex AI.
+* Configure a training job
+* Launch a training job and create a model on Vertex AI
+* Copy AutoML Video Demo Prediction Data for creating batch prediction job
+* Perform batch prediction job on the model
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/prepare-data).
+
+
+[Custom XGBoost model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb)
+
+```
+Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a scikit-learn model.
+- Upload the trained model artifacts as a `Model` resource.
+- Make a batch prediction.
+- Deploy model to a endpoint
+- Make a online prediction
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
@@ -1,19 +1,4 @@

-[Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
-
-```
-Learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
-
-The steps performed include:
-
- Track parameters and metrics for a locally trained model.
- Extract and perform analysis for all parameters and metrics within an Experiment.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata).
-
-
 [Track parameters and metrics for custom training jobs](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)

 ```
@@ -30,6 +15,21 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).


+[Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
+
+```
+Learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
+
+The steps performed include:
+
+- Track parameters and metrics for a locally trained model.
+- Extract and perform analysis for all parameters and metrics within an Experiment.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata).
+
+
 [Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb)

 ```
@@ -20,27 +20,6 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/start/automl-users#tables).


-[Evaluating batch prediction results from AutoML Video classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb)
-
-```
-Learn how to train a Vertex AI AutoML Video classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
-
-The steps performed include:
-
- Create a `Vertex AI Dataset`.
- Train a Automl Video Classification model on the `Vertex AI Dataset` resource.
- Import the trained `AutoML Vertex AI Model resource` into the pipeline.
- Run a batch prediction job inside the pipeline.
- Evaulate the AutoML model using the classification evaluation component.
- Import the classification metrics to the AutoML Vertex AI Model resource.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/prepare-data).
-
-
 [Evaluating batch prediction results from AutoML Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb)

 ```
@@ -63,30 +42,6 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/start/automl-users#tables).


-[Evaluating batch prediction results from custom tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)
-
-```
-Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
-
-The steps performed include:
-
- Create a Vertex AI `CustomTrainingJob` for training a model.
- Run the `CustomTrainingJob` 
- Retrieve and load the model artifacts.
- View the model evaluation.
- Upload the model as a Vertex AI Model resource.
- Import a pre-trained `Vertex AI model resource` into the pipeline.
- Run a `batch prediction` job in the pipeline.
- Evaulate the model using the `regression evaluation component`.
- Import the Regression Metrics to the Vertex AI model resource.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
 [AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_text_classification_model_evaluation.ipynb)

 ```
@@ -108,6 +63,27 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/classification/prepare-data).


+[Evaluating batch prediction results from AutoML Video classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb)
+
+```
+Learn how to train a Vertex AI AutoML Video classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
+
+The steps performed include:
+
+- Create a `Vertex AI Dataset`.
+- Train a Automl Video Classification model on the `Vertex AI Dataset` resource.
+- Import the trained `AutoML Vertex AI Model resource` into the pipeline.
+- Run a batch prediction job inside the pipeline.
+- Evaulate the AutoML model using the classification evaluation component.
+- Import the classification metrics to the AutoML Vertex AI Model resource.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/prepare-data).
+
+
 [Evaluating BatchPrediction results from a Custom Tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb)

 ```
@@ -134,3 +110,27 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).

+
+[Evaluating batch prediction results from custom tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)
+
+```
+Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
+
+The steps performed include:
+
+- Create a Vertex AI `CustomTrainingJob` for training a model.
+- Run the `CustomTrainingJob` 
+- Retrieve and load the model artifacts.
+- View the model evaluation.
+- Upload the model as a Vertex AI Model resource.
+- Import a pre-trained `Vertex AI model resource` into the pipeline.
+- Run a `batch prediction` job in the pipeline.
+- Evaulate the model using the `regression evaluation component`.
+- Import the Regression Metrics to the Vertex AI model resource.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
@@ -15,6 +15,128 @@ 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/bigquery-ml/docs/introduction).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).

@@ -1,14 +1,14 @@

-[AutoML image classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
+[AutoML Tabular pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)

 ```
-Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` image classification model.
+Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular classification model.

 The steps performed include:

 - Create a KFP pipeline:
    - Create a `Dataset` resource.
-    - Train an AutoML image classification `Model` resource.
+    - Train an AutoML tabular classification `Model` resource.
    - Create an `Endpoint` resource.
    - Deploys the `Model` resource to the `Endpoint` resource.
 - Compile the KFP pipeline.
@@ -21,36 +21,15 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).


-[Metrics visualization and run comparison using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb)
+[Pipeline control structures using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)

 ```
-Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
+Learn how to use the KFP SDK to build pipelines that use loops and conditionals, including nested examples.

 The steps performed include:

- Create KFP components:
-    - Generate ROC curve and confusion matrix visualizations for classification results
-    - Write metrics
- Create KFP pipelines.
- Execute KFP pipelines
- Compare metrics across pipeline runs
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-
-[Lightweight Python function-based components, and component I/O](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
-
-```
-Learn to use the KFP SDK to build lightweight Python function-based components, and then you learn to use `Vertex AI Pipelines` to execute the pipeline.
-
-The steps performed include:
-
- Build Python function-based KFP components.
- Construct a KFP pipeline.
- Pass *Artifacts* and *parameters* between components, both by path reference and by value.
- Use the `kfp.dsl.importer` method.
+- Create a KFP pipeline:
+    - Use control flow components
 - Compile the KFP pipeline.
 - Execute the KFP pipeline using `Vertex AI Pipelines`

@@ -80,16 +59,86 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training components](https://cloud.google.com/vertex-ai/docs/training/create-training-pipeline).


-[AutoML Tabular pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)
+[Training and batch prediction with BigQuery source and destinantion for a custom tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb)

 ```
-Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular classification model.
+In this tutorial, you train a scikit-learn tabular classification model and create batch prediction job for it through a Vertex AI pipeline using `google_cloud_pipeline_components`.
+
+The steps performed include:
+
+- Create a dataset in BigQuery.
+- Set some data aside from the source dataset for batch prediction.
+- Create a custom python package for training application.
+- Upload the python package to Cloud Storage.
+- Create a Vertex AI Pipeline that:
+    - creates a Vertex AI Dataset from the source dataset.
+    - trains a scikit-learn RandomForest classification model on the dataset.
+    - uploads the trained model to Vertex AI Model Registry.
+    - runs a batch prediction job with the model on the test data.
+- Check the prediction results from the destination table in BigQuery.
+- Clean up the resources created in this notebook.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction components](https://cloud.google.com/vertex-ai/docs/pipelines/batchprediction-component).
+
+
+[AutoML image classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
+
+```
+Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` image classification model.

 The steps performed include:

 - Create a KFP pipeline:
    - Create a `Dataset` resource.
-    - Train an AutoML tabular classification `Model` resource.
+    - Train an AutoML image classification `Model` resource.
+    - Create an `Endpoint` resource.
+    - Deploys the `Model` resource to the `Endpoint` resource.
+- Compile the KFP pipeline.
+- Execute the KFP pipeline using `Vertex AI Pipelines`
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
+
+
+[AutoML tabular regression pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
+
+```
+Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular regression model.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a `Dataset` resource.
+    - Train an AutoML tabular regression `Model` resource.
+    - Create an `Endpoint` resource.
+    - Deploys the `Model` resource to the `Endpoint` resource.
+- Compile the KFP pipeline.
+- Execute the KFP pipeline using `Vertex AI Pipelines`
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
+
+
+[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
+
+```
+Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a `Dataset` resource.
+    - Train an AutoML text classification `Model` resource.
    - Create an `Endpoint` resource.
    - Deploys the `Model` resource to the `Endpoint` resource.
 - Compile the KFP pipeline.
@@ -123,79 +172,6 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component).


-[Pipelines introduction for KFP](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
-
-```
-Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
-
-The steps performed include:
-
- Define and compile a `Vertex AI` pipeline.
- Specify which service account to use for a pipeline run.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-
-[AutoML tabular regression pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
-
-```
-Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular regression model.
-
-The steps performed include:
-
- Create a KFP pipeline:
-    - Create a `Dataset` resource.
-    - Train an AutoML tabular regression `Model` resource.
-    - Create an `Endpoint` resource.
-    - Deploys the `Model` resource to the `Endpoint` resource.
- Compile the KFP pipeline.
- Execute the KFP pipeline using `Vertex AI Pipelines`
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
-
-
-[Model upload, predict, and evaluate using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb)
-
-```
-Learn how to evaluate a custom model using a pipeline with components from `google_cloud_pipeline_components` and a custom pipeline component you build.
-
-The steps performed include:
-
- Upload a pre-trained model as a `Model` resource.
- Run a `BatchPredictionJob` on the `Model` resource with ground truth data.
- Generate evaluation `Metrics` artifact about the `Model` resource.
- Compare the evaluation metrics to a threshold.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model components](https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component).
-
-
-[Pipeline control structures using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)
-
-```
-Learn how to use the KFP SDK to build pipelines that use loops and conditionals, including nested examples.
-
-The steps performed include:
-
- Create a KFP pipeline:
-    - Use control flow components
- Compile the KFP pipeline.
- Execute the KFP pipeline using `Vertex AI Pipelines`
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-
 [Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb)

 ```
@@ -238,18 +214,36 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training components](https://cloud.google.com/vertex-ai/docs/pipelines/customjob-component).


-[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
+[Model upload, predict, and evaluate using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb)

 ```
-Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.
+Learn how to evaluate a custom model using a pipeline with components from `google_cloud_pipeline_components` and a custom pipeline component you build.

 The steps performed include:

- Create a KFP pipeline:
-    - Create a `Dataset` resource.
-    - Train an AutoML text classification `Model` resource.
-    - Create an `Endpoint` resource.
-    - Deploys the `Model` resource to the `Endpoint` resource.
+- Upload a pre-trained model as a `Model` resource.
+- Run a `BatchPredictionJob` on the `Model` resource with ground truth data.
+- Generate evaluation `Metrics` artifact about the `Model` resource.
+- Compare the evaluation metrics to a threshold.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Model components](https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component).
+
+
+[Lightweight Python function-based components, and component I/O](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
+
+```
+Learn to use the KFP SDK to build lightweight Python function-based components, and then you learn to use `Vertex AI Pipelines` to execute the pipeline.
+
+The steps performed include:
+
+- Build Python function-based KFP components.
+- Construct a KFP pipeline.
+- Pass *Artifacts* and *parameters* between components, both by path reference and by value.
+- Use the `kfp.dsl.importer` method.
 - Compile the KFP pipeline.
 - Execute the KFP pipeline using `Vertex AI Pipelines`

@@ -257,31 +251,58 @@ The steps performed include:

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

-&nbsp;&nbsp;&nbsp;Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).

-
-[Training and batch prediction with BigQuery source and destinantion for a custom tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_tabular_train_batch_pred_bq_pipeline.ipynb)
+[Metrics visualization and run comparison using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb)

 ```
-In this tutorial, you train a scikit-learn tabular classification model and create batch prediction job for it through a Vertex AI pipeline using `google_cloud_pipeline_components`.
+Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.

 The steps performed include:

- Create a dataset in BigQuery.
- Set some data aside from the source dataset for batch prediction.
- Create a custom python package for training application.
- Upload the python package to Cloud Storage.
- Create a Vertex AI Pipeline that:
-    - creates a Vertex AI Dataset from the source dataset.
-    - trains a scikit-learn RandomForest classification model on the dataset.
-    - uploads the trained model to Vertex AI Model Registry.
-    - runs a batch prediction job with the model on the test data.
- Check the prediction results from the destination table in BigQuery.
- Clean up the resources created in this notebook.
+- Create KFP components:
+    - Generate ROC curve and confusion matrix visualizations for classification results
+    - Write metrics
+- Create KFP pipelines.
+- Execute KFP pipelines
+- Compare metrics across pipeline runs

 ```

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

-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Batch Prediction components](https://cloud.google.com/vertex-ai/docs/pipelines/batchprediction-component).
+
+[Pipelines introduction for KFP](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
+
+```
+Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
+
+The steps performed include:
+
+- Define and compile a `Vertex AI` pipeline.
+- Specify which service account to use for a pipeline run.
+
+```
+
+&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).

@@ -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/structured_data/rapid_prototyping_bqml_automl.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/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/structured_data/rapid_prototyping_bqml_automl.ipynb\" target='_blank'>\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/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/structured_data/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/pipelines/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](https://cloud.google.com/vertex-ai/docs/start/automl-users) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction)."
+        "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)."
      ]
    },
    {
@@ -1,21 +0,0 @@
-
-[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).
-
@@ -12,9 +12,7 @@ The steps performed are:

 ```

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


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

 ```

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

@@ -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 [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)."
+        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
      ]
    },
    {
@@ -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 [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)."
+        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
      ]
    },
    {
@@ -11,9 +11,7 @@ The steps performed include:

 ```

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


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

 ```

-&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).
+&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).

@@ -63,7 +63,7 @@
        "\n",
        "This notebook showcases how to run the TabNet algorithm using Vertex AI Tabular Workflows.\n",
        "\n",
-        "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)."
+        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
      ]
    },
    {
@@ -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 [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)."
+        "Learn more about [Tabular Workflow for Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep)."
      ]
    },
    {
@@ -1,41 +1,4 @@

-[Vertex AI TensorBoard custom training with prebuilt container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb)
-
-```
-Learn how to create a custom training job using prebuilt containers, and monitor your training process on Vertex AI TensorBoard in near real time.
-
-The steps performed include:
-
-* Setup service account and Google Cloud Storage buckets.
-* Write your customized training code.
-* Package and upload your training code to Google Cloud Storage.
-* Create & launch your custom training job with Tensorboard enabled for near real time monitorning.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[Vertex AI TensorBoard integration with Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb)
-
-```
-Learn how to create a training pipeline using the KFP SDK, execute the pipeline in Vertex AI Pipelines, and monitor your training process on Vertex AI TensorBoard in near real time.
-
-The steps performed include:
-
-* Setup a service account and Google Cloud Storage buckets.
-* Construct a KFP pipeline with your custom training code.
-* Compile and execute the KFP pipeline in Vertex AI Pipelines with Tensorboard enabled for near real time monitorning.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-
 [Vertex AI TensorBoard Custom Training with custom container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb)

 ```
@@ -55,6 +18,25 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).


+[Vertex AI TensorBoard custom training with prebuilt container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb)
+
+```
+Learn how to create a custom training job using prebuilt containers, and monitor your training process on Vertex AI TensorBoard in near real time.
+
+The steps performed include:
+
+* Setup service account and Google Cloud Storage buckets.
+* Write your customized training code.
+* Package and upload your training code to Google Cloud Storage.
+* Create & launch your custom training job with Tensorboard enabled for near real time monitorning.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
 [Profile model training performance using Vertex AI TensorBoard Profiler](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_profiler_custom_training.ipynb)

 ```
@@ -71,3 +53,21 @@ The steps performed include:

 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard Profiler](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-profiler).

+
+[Vertex AI TensorBoard integration with Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tensorboard/tensorboard_vertex_ai_pipelines_integration.ipynb)
+
+```
+Learn how to create a training pipeline using the KFP SDK, execute the pipeline in Vertex AI Pipelines, and monitor your training process on Vertex AI TensorBoard in near real time.
+
+The steps performed include:
+
+* Setup a service account and Google Cloud Storage buckets.
+* Construct a KFP pipeline with your custom training code.
+* Compile and execute the KFP pipeline in Vertex AI Pipelines with Tensorboard enabled for near real time monitorning.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
+
@@ -1,4 +1,22 @@

+[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)

 ```
@@ -1,167 +1,4 @@

-[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)
-
-```
-Learn how to train and deploy an AutoML sentiment analysis model, and make predictions.
-
-The steps performed are:
-
- Loading the required data. 
- Preprocessing the data.
- Selecting the required data for the model.
- Loading the dataset into Vertex AI managed datasets.
- Training a sentiment model using AutoML Text training.
- Evaluating the model.
- Deploying the model on Vertex AI.
- Getting predictions.
- Clean up.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/tutorials/text-classification-automl/training).
-
-
-[Interactive exploratory analysis of BigQuery data in a notebook](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb)
-
-```
-Learn about various ways to explore and gain insights from BigQuery data in a Jupyter notebook environment.
-
-The steps performed include:
-
- Using Python & SQL to query public data in BigQuery
- Exploring the dataset using BigQuery INFORMATION_SCHEMA
- Creating interactive elements to help explore interesting parts of the data
- Doing some exploratory correlation and time series analysis
- Creating static and interactive outputs (data tables and plots) in the notebook
- Saving some outputs to Cloud Storage
-
-```
-
-&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](https://cloud.google.com/bigquery).
-
-
-[Forecasting retail demand with Vertex AI and BigQuery ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/demand_forecasting/forecasting-retail-demand.ipynb)
-
-```
-Learn how to build ARIMA (Autoregressive integrated moving average) model from BigQuery ML on retail data
-
-The steps performed include:
-
-* Explore data
-* Model with BigQuery and the ARIMA model
-* Evaluate the model
-* Evaluate the model results using BigQuery ML (on training data)
-* Evalute the model results - MAE, MAPE, MSE, RMSE (on test data)
-* Use the executor feature
-
-```
-
-&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/bigquery-ml/docs/managing-models-vertex).
-
-[Predictive Maintenance using Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/predictive_maintainance/predictive_maintenance_usecase.ipynb)
-
-```
-
-The steps performed are:
-
- Loading the required dataset from a Cloud Storage bucket.
- Analyzing the fields present in the dataset.
- Selecting the required data for the predictive maintenance model.
- Training an XGBoost regression model for predicting the remaining useful life.
- Evaluating the model.
- Running the notebook end-to-end as a training job using Executor.
- Deploying the model on Vertex AI.
- Clean up.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[Telecom subscriber churn prediction on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/subscriber_churn_prediction/telecom-subscriber-churn-prediction.ipynb)
-
-```
-This tutorial shows you how to do exploratory data analysis, preprocess data, train, deploy and get predictions from a churn prediction model on a tabular churn dataset.
-
-The steps performed include:
-
- Load data from a Cloud Storage path
- Perform exploratory data analysis (EDA)
- Preprocess the data
- Train a scikit-learn model
- Evaluate the scikit-learn model
- Save the model to a Cloud Storage path
- Create a model and an endpoint in Vertex AI
- Deploy the trained model to an endpoint
- Generate predictions and explanations on test data from the hosted model
- Undeploy the model resource
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
-
-
-[SparkML with Dataproc and BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/spark/spark_ml.ipynb)
-
-```
-This tutorial runs an Apache SparkML job that fetches data from the BigQuery dataset, performs exploratory data analysis, cleans the data, executes feature engineering, trains the model, evaluates the model, outputs results, and saves the model to a Cloud Storage bucket.
-
-The steps performed are:
-
- Sets up a Google Cloud project and Dataproc cluster.
- Creates a Cloud Storage bucket and a BigQuery dataset.
- Configures the spark-bigquery-connector.
- Ingests BigQuery data into a Spark DataFrame.
- Performa Exploratory Data Analysis (EDA).
- Visualizes the data with samples.
- Cleans the data.
- Selects features.
- Trains the model.
- Outputs results.
- Saves the model to a Cloud Storage bucket.
- Deletes the resources created for the tutorial.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Dataproc](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component).
-
-
-[Digest and analyze data from BigQuery with Dataproc](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/spark/spark_bigquery.ipynb)
-
-```
-This notebook tutorial runs an Apache Spark job that fetches data from the BigQuery "GitHub Activity Data" dataset, queries the data, and then writes the results back to BigQuery.
-
-The steps performed are:
-
- Setting up a Google Cloud project and Dataproc cluster.
- Configuring the spark-bigquery-connector.
- Ingesting data from BigQuery into a Spark DataFrame.
- Preprocessing ingested data.
- Querying the most frequently used programming language in monoglot repos.
- Querying the average size (MB) of code in each language stored in monoglot repos.
- Querying the languages files most frequently found together in polyglot repos.
- Writing the query results back into BigQuery.
- Deleting the resources created for this notebook tutorial.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Dataproc](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component).
-
-
 [Train a multi-class classification model for ads-targeting](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/ads_targetting/training-multi-class-classification-model-for-ads-targeting-usecase.ipynb)

 ```
@@ -184,52 +21,6 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).


-[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)
-
-```
-This tutorial shows you how to do exploratory data analysis, preprocess data, train model, evaluate model, deploy model, configure What-If Tool.
-
-The steps performed include:
-
-* Load the dataset from BigQuery using the "BigQuery in Notebooks" integration.
-* Analyze the dataset.
-* Preprocess the features in the dataset.
-* Build a random forest classifier model that predicts whether a product will get sold in the next 60 days.
-* Evaluate the model.
-* Deploy the model using Vertex AI.
-* Configure and test with the What-If Tool.
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
-[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)
-
-```
-This tutorial demonstrates data analysis and model-building using a synthetic financial dataset.
-
-The steps performed include:
-
- Installation of required libraries
- Reading the dataset from a Cloud Storage bucket
- Performing exploratory analysis on the dataset
- Preprocessing the dataset
- Training a random forest model using scikit-learn
- Saving the model to a Cloud Storage bucket
- Creating a Vertex AI model resource and deploying to an endpoint
- Running the What-If Tool on test data
- Un-deploying the model and cleaning up the model resources
-
-```
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-
-
 [Taxi fare prediction using the Chicago Taxi Trips dataset](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/chicago_taxi_fare_prediction/chicago_taxi_fare_prediction.ipynb)

 ```
@@ -253,6 +44,72 @@ The steps performed include:
 &nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).


+[Forecasting retail demand with Vertex AI and BigQuery ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/demand_forecasting/forecasting-retail-demand.ipynb)
+
+```
+Learn how to build ARIMA (Autoregressive integrated moving average) model from BigQuery ML on retail data
+
+The steps performed include:
+
+* Explore data
+* Model with BigQuery and the ARIMA model
+* Evaluate the model
+* Evaluate the model results using BigQuery ML (on training data)
+* Evalute the model results - MAE, MAPE, MSE, RMSE (on test data)
+* Use the executor feature
+
+```
+
+&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/bigquery-ml/docs/managing-models-vertex).
+
+
+[Interactive exploratory analysis of BigQuery data in a notebook](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb)
+
+```
+Learn about various ways to explore and gain insights from BigQuery data in a Jupyter notebook environment.
+
+The steps performed include:
+
+- Using Python & SQL to query public data in BigQuery
+- Exploring the dataset using BigQuery INFORMATION_SCHEMA
+- Creating interactive elements to help explore interesting parts of the data
+- Doing some exploratory correlation and time series analysis
+- Creating static and interactive outputs (data tables and plots) in the notebook
+- Saving some outputs to Cloud Storage
+
+```
+
+&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).
+
+
+[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)
+
+```
+This tutorial demonstrates data analysis and model-building using a synthetic financial dataset.
+
+The steps performed include:
+
+- Installation of required libraries
+- Reading the dataset from a Cloud Storage bucket
+- Performing exploratory analysis on the dataset
+- Preprocessing the dataset
+- Training a random forest model using scikit-learn
+- Saving the model to a Cloud Storage bucket
+- Creating a Vertex AI model resource and deploying to an endpoint
+- Running the What-If Tool on test data
+- Un-deploying the model and cleaning up the model resources
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
 [Churn prediction for game developers using Google Analytics 4 and BigQuery ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb)

 ```
@@ -270,7 +127,52 @@ 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/bigquery-ml/docs/managing-models-vertex).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+
+
+[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)
+
+```
+This tutorial shows you how to do exploratory data analysis, preprocess data, train model, evaluate model, deploy model, configure What-If Tool.
+
+The steps performed include:
+
+* Load the dataset from BigQuery using the "BigQuery in Notebooks" integration.
+* Analyze the dataset.
+* Preprocess the features in the dataset.
+* Build a random forest classifier model that predicts whether a product will get sold in the next 60 days.
+* Evaluate the model.
+* Deploy the model using Vertex AI.
+* Configure and test with the What-If Tool.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+
+
+[Predictive Maintenance using Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/predictive_maintainance/predictive_maintenance_usecase.ipynb)
+
+```
+Learn how to the executor feature of Vertex AI Workbench to automate a workflow to train and deploy a model.
+
+The steps performed are:
+
+- Loading the required dataset from a Cloud Storage bucket.
+- Analyzing the fields present in the dataset.
+- Selecting the required data for the predictive maintenance model.
+- Training an XGBoost regression model for predicting the remaining useful life.
+- Evaluating the model.
+- Running the notebook end-to-end as a training job using Executor.
+- Deploying the model on Vertex AI.
+- Clean up.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).


 [Analysis of pricing optimization on CDM Pricing Data](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/pricing_optimization/pricing-optimization.ipynb)
@@ -292,5 +194,105 @@ 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/bigquery-ml/docs/managing-models-vertex).
+&nbsp;&nbsp;&nbsp;Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+
+
+[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)
+
+```
+Learn how to train and deploy an AutoML sentiment analysis model, and make predictions.
+
+The steps performed are:
+
+- Loading the required data. 
+- Preprocessing the data.
+- Selecting the required data for the model.
+- Loading the dataset into Vertex AI managed datasets.
+- Training a sentiment model using AutoML Text training.
+- Evaluating the model.
+- Deploying the model on Vertex AI.
+- Getting predictions.
+- Clean up.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/tutorials/text-classification-automl/training).
+
+
+[Digest and analyze data from BigQuery with Dataproc](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/spark/spark_bigquery.ipynb)
+
+```
+This notebook tutorial runs an Apache Spark job that fetches data from the BigQuery "GitHub Activity Data" dataset, queries the data, and then writes the results back to BigQuery.
+
+The steps performed are:
+
+- Setting up a Google Cloud project and Dataproc cluster.
+- Configuring the spark-bigquery-connector.
+- Ingesting data from BigQuery into a Spark DataFrame.
+- Preprocessing ingested data.
+- Querying the most frequently used programming language in monoglot repos.
+- Querying the average size (MB) of code in each language stored in monoglot repos.
+- Querying the languages files most frequently found together in polyglot repos.
+- Writing the query results back into BigQuery.
+- Deleting the resources created for this notebook tutorial.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Dataproc](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component).
+
+
+[SparkML with Dataproc and BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/spark/spark_ml.ipynb)
+
+```
+This tutorial runs an Apache SparkML job that fetches data from the BigQuery dataset, performs exploratory data analysis, cleans the data, executes feature engineering, trains the model, evaluates the model, outputs results, and saves the model to a Cloud Storage bucket.
+
+The steps performed are:
+
+- Sets up a Google Cloud project and Dataproc cluster.
+- Creates a Cloud Storage bucket and a BigQuery dataset.
+- Configures the spark-bigquery-connector.
+- Ingests BigQuery data into a Spark DataFrame.
+- Performa Exploratory Data Analysis (EDA).
+- Visualizes the data with samples.
+- Cleans the data.
+- Selects features.
+- Trains the model.
+- Outputs results.
+- Saves the model to a Cloud Storage bucket.
+- Deletes the resources created for the tutorial.
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Dataproc](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component).
+
+
+[Telecom subscriber churn prediction on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/subscriber_churn_prediction/telecom-subscriber-churn-prediction.ipynb)
+
+```
+This tutorial shows you how to do exploratory data analysis, preprocess data, train, deploy and get predictions from a churn prediction model on a tabular churn dataset.
+
+The steps performed include:
+
+- Load data from a Cloud Storage path
+- Perform exploratory data analysis (EDA)
+- Preprocess the data
+- Train a scikit-learn model
+- Evaluate the scikit-learn model
+- Save the model to a Cloud Storage path
+- Create a model and an endpoint in Vertex AI
+- Deploy the trained model to an endpoint
+- Generate predictions and explanations on test data from the hosted model
+- Undeploy the model resource
+
+```
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
+
+&nbsp;&nbsp;&nbsp;Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).

@@ -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 [BigQuery](https://cloud.google.com/bigquery)."
+        "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)."
      ]
    },
    {
@@ -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 [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
+        "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)."
      ]
    },
    {
@@ -95,6 +95,8 @@
        "### Objective\n",
        "<a name=\"section-2\"></a>\n",
        "\n",
+        "In this tutorial, you learn how to the executor feature of Vertex AI Workbench to automate a workflow to train and deploy a model.\n",
+        "\n",
        "This tutorial uses the following Google Cloud ML services:\n",
        "\n",
        "- `Vertex AI Training`\n",
@@ -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 [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
+        "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)."
      ]
    },
    {
Author	SHA1	Message	Date
Andrew Ferlitsch	e5d9a0db51	migration: experiments	2023-01-19 22:19:39 +00:00
Andrew Ferlitsch	27918651ab	migration: experiments	2023-01-19 20:55:00 +00:00
Andrew Ferlitsch	c4d632b797	migration: experiments	2023-01-19 20:45:54 +00:00
Andrew Ferlitsch	610dfda3ab	migration: experiments	2023-01-19 20:43:39 +00:00
Andrew Ferlitsch	1e8dd46334	migration: experiments	2023-01-19 20:33:38 +00:00
Andrew FerlitschandGitHub	1551ca9435	migration: experiments (#1472 ) * migration: experiments * migration: experiments * debug: experiments	2023-01-19 12:03:28 -08:00
Andrew FerlitschandGitHub	80fcd2904f	migration: bqml (#1475 ) * fix: require code review * migration: BQML	2023-01-18 07:26:53 -08:00
Daniel Elias BecerraandGitHub	e974c034ba	Matching engine tutorial - add networking troubleshooting and updates to notebook (#1465 ) * matching engine tutorial add networking troubleshooting * format check changes * Change year 2021 to 2023, replace colab, github and workbench links with new template style * Replace all occurences of ANN and ANN service with matching_engine or Vertex AI Matching Engine to reflect updated product name * Update Before you Begin section to follow notebook template and add more organization to it * Update installation of Vertex AI SDK python library from preview to GA version * Remove outdated set project id section * Add Authentication section from notebook template * Update create bucket section to incorporate notebook template guidelines * Fix format issues * Fix format issues * Fix issues when trying the notebook changes, ordered sections and updated some outdated commands * Add troubleshooting comment for service networking role for worbench instance to create vpc peering * Add troubleshooting comment for service networking role for worbench instance to create vpc peering * Revert "Add troubleshooting comment for service networking role for worbench instance to create vpc peering" This reverts commit `ed418a392a`. * Add wait to deploying index * Add wait to deploying index * remove redundant import * Format file	2023-01-17 09:03:18 -08:00
Aleksey VlasenkoandGitHub	d2d4493397	fixed T5x sample links (#1473 )	2023-01-13 17:58:33 -08:00
Andrew FerlitschandGitHub	0cd6146a6c	fix: restore requirements.txt (#1468 )	2023-01-13 09:52:24 -08:00
Andrew FerlitschandGitHub	b61395f465	migration: distributed training (#1466 ) * migration: distributed training * migrate: code review	2023-01-13 09:50:43 -08:00
Andrew FerlitschandGitHub	649b209577	upgrade: revised index (#1463 ) * upgrade: revised index * upgrade: revised index * upgrade: revised index	2023-01-12 16:29:20 -08:00
Andrew FerlitschandGitHub	f42a184171	migration: automl (#1455 )	2023-01-12 09:29:25 -08:00
Andrew FerlitschandGitHub	03f0647b76	migration: MM notebook (#1445 ) * migration: MM notebook * migration: fix USER_EMAIL	2023-01-12 09:28:43 -08:00
Andrew FerlitschandGitHub	1f39732ae9	migration: distributed training (#1460 )	2023-01-11 22:38:26 -08:00
Andrew FerlitschandGitHub	7dd0b31b58	migration: experiments (#1461 )	2023-01-11 18:16:34 -08:00
Andrew FerlitschandGitHub	ff843173cf	Autoindex official 2 (#1459 ) * fix: update linkbacks to vertex pages * fix: update linkbacks to vertex pages * fix: update linkbacks to vertex pages	2023-01-11 16:53:28 -08:00
Andrew FerlitschandGitHub	da19b116e9	fix: update linkbacks to vertex pages (#1458 )	2023-01-11 16:30:10 -08:00
Andrew FerlitschandGitHub	d8b365dfd4	fix: update the linkback (#1457 )	2023-01-11 15:02:14 -08:00
Andrew FerlitschandGitHub	1247c80fed	migration: bqml (#1456 )	2023-01-11 14:53:13 -08:00
Aleksey Vlasenko GitHub Aleksey Vlasenko	2cf2bf1080	Adding sample T5x sample for optimized TensorFlow runtime (#1453 ) * adding T5x sample * update for benchmark params * update for benchmark params * updated model GCS buckets for optimized TF runtime T5x sample * added GPU accelerators for deployment pool in Vertex shared VM sample * final updates for T5x sample * addressed PR feedback Co-authored-by: Aleksey Vlasenko <alekseyv@google.com>	2023-01-11 13:32:53 -08:00
Andrew FerlitschandGitHub	5e9e8139c1	Update get_started_with_model_monitoring_xgboost.ipynb	2023-01-11 12:10:46 -08:00
Andrew FerlitschandGitHub	0728a0036f	Update get_started_with_model_monitoring_setup.ipynb	2023-01-11 12:10:11 -08:00
Andrew FerlitschandGitHub	aa52d21643	Update get_started_with_model_monitoring_custom_tf_serving.ipynb	2023-01-11 12:09:18 -08:00
Andrew FerlitschandGitHub	103888d75e	Update get_started_with_model_monitoring_custom.ipynb	2023-01-11 12:08:33 -08:00
Andrew FerlitschandGitHub	13d0d5d9b0	Update get_started_bq_datasets.ipynb	2023-01-11 12:06:47 -08:00
Andrew FerlitschandGitHub	79c6669686	Update get_started_with_data_labeling.ipynb	2023-01-11 12:06:20 -08:00
Andrew FerlitschandGitHub	dceb0c4c1c	Update get_started_bq_datasets.ipynb	2023-01-11 12:04:31 -08:00
Andrew FerlitschandGitHub	0c9cdca713	migration: MM notebook (#1449 ) * migration: MM notebook * migration: MM notebook	2023-01-10 20:58:19 -08:00
Andrew FerlitschandGitHub	6124092681	migration: MM notebook (#1447 ) * migration: MM notebook * migration: MM notebook	2023-01-10 18:39:45 -08:00
Andrew FerlitschandGitHub	629e739327	migration: MM notebook (#1446 ) * migration: MM notebook * migration: MM notebook	2023-01-10 17:51:00 -08:00
Andrew FerlitschandGitHub	b68cbd8255	migration: move to pipelines folder (#1452 )	2023-01-10 16:44:17 -08:00
Andrew FerlitschandGitHub	7fec30c12f	migration: MM notebook (#1448 )	2023-01-10 16:31:14 -08:00
Andrew FerlitschandGitHub	a754843c39	migration: MM notebook (#1444 ) * migration: MM notebook * migration: MM notebook * migration: MM notebook	2023-01-10 15:33:26 -08:00
reznitskii GitHub Max Reznitskii	436db4a35b	Fixed documentation links (#1450 ) Co-authored-by: Max Reznitskii <reznitskii@google.com>	2023-01-10 15:22:58 -08:00
Ivan NardiniandGitHub	8dba2d040b	anomaly detection notebook review (#1440 ) * fix some minor issues * linter passed	2023-01-10 11:50:28 -08:00
Kelsi LakeyandGitHub	560dd9da15	[Community] Added image classification pipeline sample for Ready-to-Go Vertex project (#1404 ) * Add image classification pipeline components * Update CODEOWNERS file with image_ml_model_training * [Community] Added image classification pipeline sample for Ready-to-Go Vertex project * Remove unnecessary component download	2023-01-10 11:47:53 -08:00
Nicolas WipfliandGitHub	8ec17d6aca	Workaround for shapely (#1397 ) Without this workaround, the command "from google.cloud import aiplatform as vertex_ai" fails due to the following issue: https://github.com/googleapis/python-aiplatform/issues/1852	2023-01-10 11:46:32 -08:00
stewart-co GitHub Andrew Ferlitsch	f545282c36	Cohere demo (#1391 ) * Adding Cohere Embedding Demo * Update cohere_embedding_with_matching_engine.ipynb * Update cohere_embedding_with_matching_engine.ipynb * Update CODEOWNERS * Update CODEOWNERS * Update CODEOWNERS * Update cohere_embedding_with_matching_engine.ipynb * Update cohere_embedding_with_matching_engine.ipynb Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2023-01-10 11:43:16 -08:00
reznitskii GitHub Max Reznitskii	7e4e14e084	Fixed links to documentation (#1438 ) Co-authored-by: Max Reznitskii <reznitskii@google.com>	2023-01-09 15:32:56 -08:00
Douglass ChenandGitHub	be5933115d	Add Colab notebooks to run text classification model pipelines (#1360 ) * Add Cloud natural language pipeline colab notebook * Add ready-to-go text classification pipeline colab notebook * Ran reformatting scripts on text classification pipeline colab notebooks * Update CODEOWNERS files * Fix order of cells in cloud_natural_language_pipeline.ipynb * Remove unused variables via linter for text classification colabs; fix classification variable for preprocessing component * Minor fix: remove GCPC version requirement * Minor fix: remove outputs * fix formatting with nbfmt * move ready-to-go pipeline to notebooks/community * fix link * update CODEOWNERS * move text classification colabs to notebooks/community/pipelines * Address initial comments on NL notebook * Remove commented lines in NL notebook * minor cell formatting * clear outputs * minor changes to NL notebook * address comments for ready-to-go pipeline * run linter locally * add pipeline description to NL pipeline * run linter locally (PR check could not lint) * Add cell to examine metrics, update kernel restart cell from official template * lint	2023-01-09 13:12:19 -08:00
reznitskiiandGitHub	be46140138	Update README.md (#1433 )	2023-01-09 08:59:47 -08:00
Andrew FerlitschandGitHub	79f4dbaafe	Autoindex official 2 (#1432 ) * fix: alpha sort * fix: alpha sort * fix: alpha sort	2023-01-08 12:22:32 -08:00
Andrew FerlitschandGitHub	9577f8324c	Autoindex official 2 (#1431 ) * fix: alpha sort * fix: alpha sort	2023-01-08 12:17:06 -08:00
Andrew FerlitschandGitHub	01274fe767	fix: alpha sort (#1430 )	2023-01-08 12:13:44 -08:00
Andrew FerlitschandGitHub	1690b07e4a	Autoindex official (#1429 ) * upgrade: prep for auto docs index * upgrade: prep for auto docs index * upgrade: prep work of web index * upgrade: autoindex, map dirnames to tags * upgrade: autogen index, folder to tag * upgrade: autogen index, folder to tag * upgrade: fine-tune layout for webdoc * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * feat: CL var replacements * fix: tuning index * fix: tuning index * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: index tuning * tuning: linkbak for repo index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * fix: notebook objective * fix: notebook objective * fix: alpha sort	2023-01-08 12:04:41 -08:00
Andrew FerlitschandGitHub	6b0de60a5c	Autoindex official (#1428 ) * upgrade: prep for auto docs index * upgrade: prep for auto docs index * upgrade: prep work of web index * upgrade: autoindex, map dirnames to tags * upgrade: autogen index, folder to tag * upgrade: autogen index, folder to tag * upgrade: fine-tune layout for webdoc * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * upgrade: fine-tuning tags and linkbacks * feat: CL var replacements * fix: tuning index * fix: tuning index * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: fine tune indexing * fix: index tuning * tuning: linkbak for repo index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * tuning: README index * fix: notebook objective * fix: notebook objective	2023-01-07 12:53:35 -08:00