migration: move to pipelines folder

.cloud-build/requirements.txt

@@ -11,3 +11,4 @@ google-cloud-storage
 google-cloud-build
 ratemate
 GitPython
+google-api-core==2.10

notebooks/community/ml_ops/stage5/get_started_with_vertex_endpoint_and_shared_vm.ipynb

@@ -1028,9 +1028,6 @@
         "deployment_resource_pool.dedicated_resources.min_replica_count = MIN_NODES\n",
         "deployment_resource_pool.dedicated_resources.max_replica_count = MAX_NODES\n",
         "deployment_resource_pool.dedicated_resources.machine_spec.machine_type = DEPLOY_COMPUTE\n",
-        "if DEPLOY_NGPU:\n",
-        "    deployment_resource_pool.dedicated_resources.machine_spec.accelerator_type = DEPLOY_GPU\n",
-        "    deployment_resource_pool.dedicated_resources.machine_spec.accelerator_count = DEPLOY_NGPU\n",
         "\n",
         "request = aip_beta.CreateDeploymentResourcePoolRequest(\n",
         "    parent=f\"projects/{PROJECT_ID}/locations/{REGION}\",\n",

notebooks/community/vertex_endpoints/optimized_tensorflow_runtime/benchmark.py

@@ -112,7 +112,7 @@ def benchmark(
 
     results = []
     for qps in qps_list:
-        num_requests = int(max(qps * duration_sec, 10))
+        num_requests = max(qps * duration_sec, 10)
         requests_for_qps = list(
             itertools.islice(itertools.cycle(requests), num_requests)
         )

notebooks/notebook_template_review.py

@@ -12,10 +12,6 @@
         --errors-codes: A list of error codes to report errors. Otherwise, all errors are reported.
         --errors-csv: Report errors in CSV format
         
-        # options for automatic fixing
-        --fix: Automatic fix
-        --fix-codes: A list of fix codes to fix. Otherwise, all fix codes are enabled.
-        
         # index generatation
         --repo: Generate index in markdown format
         --web: Generate index in HTML format
@@ -23,7 +19,6 @@
         --desc: Add description to index
         --steps: Add steps to index
         --uses: Add "resources" used to index
-        --linkback: Add linkback to index
         
     Format of CSV file for notebooks to review:
     

notebooks/official/automl/README.md

@@ -14,7 +14,7 @@ The steps performed include the following:
 
 ```
 
-   Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+   Learn more about [Tabular classification](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
 
 
 [Create, train, and deploy an AutoML text classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-text-classification.ipynb)
@@ -34,7 +34,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Classification for text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text).
+   Learn more about [AutoML Text classification](https://cloud.google.com/vertex-ai/docs/text-data/classification/train-model).
 
 
 [Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb)
@@ -54,7 +54,9 @@ The steps performed are:
 
 ```
 
-   Learn more about [BQML ARIMA+ forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview).
+   Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).
+
+   Learn more about [BQML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting-arima/overview).
 
 
 [AutoML Tabular Workflow pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb)
@@ -69,26 +71,7 @@ The steps performed are:
 
 ```
 
-   Learn more about [Tabular Workflow for E2E AutoML](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl).
-
-
-[Get started with AutoML Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/get_started_automl_training.ipynb)
-
-```
-Learn how to use `AutoML` for training with `Vertex AI`.
-
-The steps performed include:
-
-- Train an image model
-- Export the image model as an edge model
-- Train a tabular model
-- Export the tabular model as a cloud model
-- Train a text model
-- Train a video model
-
-```
-
-   Learn more about [Vertex AI for AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users).
+   Learn more about [AutoML Tabular Workflows](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl).
 
 
 [AutoML training hierarchical forecasting for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_forecasting_hierarchical_batch.ipynb)
@@ -107,7 +90,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Hierarchical forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/hierarchical).
+   Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).
 
 
 [AutoML training image object detection model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb)
@@ -124,7 +107,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Object detection for image data](https://cloud.google.com/vertex-ai/docs/training-overview#object_detection_for_images).
+   Learn more about [AutoML Image](https://cloud.google.com/vertex-ai/docs/image-data/object-detection/train-model).
 
 
 [AutoML tabular forecasting model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
@@ -141,7 +124,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Forecasting for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview).
+   Learn more about [AutoML Forecasting](https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/tutorials-samples).
 
 
 [AutoML training tabular regression model for batch prediction using BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb)
@@ -160,7 +143,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+   Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data).
 
 
 [AutoML training tabular regression model for online prediction using BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb)
@@ -179,7 +162,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Regression for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview).
+   Learn more about [AutoML Tabular](https://cloud.google.com/vertex-ai/docs/training-overview#tabular_data).
 
 
 [AutoML training text entity extraction model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb)
@@ -198,7 +181,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text).
+   Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/entity-extraction/train-model).
 
 
 [Training an AutoML text sentiment analysis model for online predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb)
@@ -218,7 +201,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).
+   Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/sentiment-analysis/train-model).
 
 
 [AutoML training video action recognition model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb)
@@ -235,7 +218,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Action recognition for video data](https://cloud.google.com/vertex-ai/docs/training-overview#action_recognition_for_videos).
+   Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/action-recognition/train-model).
 
 
 [AutoML training video classification model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb)
@@ -252,7 +235,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos).
+   Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/classification/train-model).
 
 
 [AutoML training video object tracking model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb)
@@ -269,5 +252,5 @@ The steps performed include:
 
 ```
 
-   Learn more about [Object tracking for video data](https://cloud.google.com/vertex-ai/docs/training-overview#object_tracking_for_videos).
+   Learn more about [AutoML Video](https://cloud.google.com/vertex-ai/docs/video-data/object-tracking/train-model).
 

notebooks/official/bigquery_ml/README.md

@@ -15,25 +15,5 @@ The steps performed include:
 
 ```
 
-   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`
-
-```
-
-   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+   Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction).
 

notebooks/official/bigquery_ml/bqml-online-prediction.ipynb

@@ -63,7 +63,7 @@
         "\n",
         "This notebook is aimed at data analysts and data scientists who have data in BigQuery, want to train a model using BigQuery ML, register the model to Vertex AI Model Registry, and deploy it to an endpoint for real-time prediction. \n",
         "\n",
-        "Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction)."
       ]
     },
     {

notebooks/official/datasets/get_started_bq_datasets.ipynb

@@ -29,7 +29,7 @@
         "id": "title:generic,gcp"
       },
       "source": [
-        "# Get started with BigQuery datasets\n",
+        "# E2E ML on GCP: MLOps stage 1 : data management: get started with BigQuery datasets\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -61,7 +61,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers data management: get started with BigQuery datasets.\n",
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with BigQuery datasets.\n",
         "\n",
         "Learn more about [BigQuery Datasets](https://cloud.google.com/bigquery/docs/datasets-intro)."
       ]

notebooks/official/datasets/get_started_with_data_labeling.ipynb

@@ -29,7 +29,7 @@
         "id": "title:generic,gcp"
       },
       "source": [
-        "# Get started with Vertex AI Data Labeling\n",
+        "# E2E ML on GCP: MLOps stage 1 : formalization: get started with Vertex AI Data Labeling\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -62,7 +62,7 @@
         "## Overview\n",
         "\n",
         "\n",
-        "This tutorial demonstrates how to use Vertex AI in production. This tutorial covers data management: get started with Vertex AI Data Labeling service.\n",
+        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 1 : data management: get started with Vertex AI Data Labeling service.\n",
         "\n",
         "Learn more about [Vertex AI Data Labeling](https://cloud.google.com/vertex-ai/docs/datasets/data-labeling-job)."
       ]

notebooks/official/experiments/README.md

@@ -55,36 +55,3 @@ The steps performed include:
 
    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
-
-```
-
-   Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
-
-   Learn more about [Vertex ML Metadata](https://cloud.google.com/vertex-ai/docs/ml-metadata).
-
-   Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
-

notebooks/official/experiments/get_started_with_vertex_experiments.ipynb

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

notebooks/official/model_monitoring/README.md

@@ -15,128 +15,6 @@ The steps performed include:
    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.
-
-```
-
-   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.
-
-```
-
-   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.
-
-```
-
-   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.
-
-```
-
-   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.
-
-```
-
-   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.
-
-```
-
-   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)
 
 ```

notebooks/official/model_registry/README.md

@@ -17,5 +17,5 @@ The steps performed include:
 
    Learn more about [Vertex AI Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).
 
-   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+   Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/introduction).
 

notebooks/official/pipelines/README.md

@@ -285,24 +285,3 @@ The steps performed include:
 
    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.
-
-```
-
-   Learn more about [AutoML components](https://cloud.google.com/vertex-ai/docs/pipelines/vertex-automl-component).
-
-   Learn more about [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component).
-

notebooks/official/tabnet/README.md

@@ -12,7 +12,9 @@ The steps performed are:
 
 ```
 
-   Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+   Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+
+   Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
 
 
 [Vertex AI TabNet](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb)
@@ -30,5 +32,7 @@ The steps performed are:
 
 ```
 
-   Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+   Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+
+   Learn more about [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
 

notebooks/official/tabnet/ai-explanations-tabnet-algorithm.ipynb

@@ -65,7 +65,7 @@
         "\n",
         "The goal of the tutorial is to provide a sample plotting tool to visualize the output of TabNet, which is helpful in explaining the algorithm.\n",
         "\n",
-        "Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet)."
+        "Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet) and [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)."
       ]
     },
     {

notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb

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

notebooks/official/tabular_workflows/README.md

@@ -11,7 +11,9 @@ The steps performed include:
 
 ```
 
-   Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+   Learn more about [Vertex AI TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
+
+   Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
 
 
 [Wide & Deep Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb)
@@ -26,5 +28,7 @@ The steps performed include:
 
 ```
 
-   Learn more about [Tabular Workflow for Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep).
+   Learn more about [Vertex AI Wide & Deep](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/wide-and-deep).
+
+   Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
 

notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb

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

notebooks/official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb

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

notebooks/official/training/README.md

@@ -1,22 +1,4 @@
 
-[Get started with Vertex AI Distributed Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/get_started_with_vertex_distributed_training.ipynb)
-
-```
-Learn how to use `Vertex AI Distributed Training` for when training with `Vertex AI`.
-
-The steps performed include:
-
-- `MirroredStrategy`: Train on a single VM with multiple GPUs.
-- `MultiWorkerMirroredStrategy`: Train on multiple VMs with automatic setup of replicas.
-- `MultiWorkerMirroredStrategy`: Train on multiple VMs with fine grain control of replicas.
-- `ReductionServer`: Train on multiple VMS and sync updates across VMS with `Vertex AI Reduction Server`.
-- `TPUTraining`: Train with multiple Cloud TPUs.
-
-```
-
-   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)
 
 ```

notebooks/official/workbench/README.md

@@ -83,7 +83,7 @@ The steps performed include:
 
    Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
-   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+   Learn more about [BigQuery](https://cloud.google.com/bigquery).
 
 
 [Build a fraud detection model on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb)
@@ -127,7 +127,7 @@ The steps performed include:
 
    Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
-   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+   Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex).
 
 
 [Inventory prediction on ecommerce data using Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/inventory-prediction/inventory_prediction.ipynb)
@@ -194,7 +194,7 @@ The steps performed include:
 
    Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
-   Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml).
+   Learn more about [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex).
 
 
 [Sentiment Analysis using AutoML Natural Language and Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/sentiment_analysis/Sentiment_Analysis.ipynb)

notebooks/official/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb

@@ -63,7 +63,7 @@
         "\n",
         "This notebook is written for data analysts and data scientists who have data in BigQuery and want to perform exploratory data analysis to gather insights from that data in an interactive environment.\n",
         "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery](https://cloud.google.com/bigquery)."
       ]
     },
     {

notebooks/official/workbench/gaming_churn_prediction/churn_prediction_for_game_developers.ipynb

@@ -92,7 +92,7 @@
         "\n",
         "This tutorial shows you how to train, evaluate a propensity model in BigQuery ML to predict user retention on a mobile game, based on app measurement data from Google Analytics 4.\n",
         "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
       ]
     },
     {

notebooks/official/workbench/pricing_optimization/pricing-optimization.ipynb

@@ -87,7 +87,7 @@
         "\n",
         "*Note: This notebook file was developed to run in a [Vertex AI Workbench managed notebooks](https://console.cloud.google.com/vertex-ai/workbench/list/managed) instance using the Python (Local) kernel. Some components of this notebook may not work in other notebook environments.*\n",
         "\n",
-        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and Learn more about [BigQuery ML](https://cloud.google.com/vertex-ai/docs/beginner/bqml)."
+        "Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction) and [BigQuery ML](https://cloud.google.com/bigquery-ml/docs/managing-models-vertex)."
       ]
     },
     {