fix: restore requirements.txt

notebooks/community/vertex_endpoints/optimized_tensorflow_runtime/t5x_base_optimized_online_prediction.ipynb

@@ -40,18 +40,18 @@
       "source": [
         "<table align=\"left\">\n",
         "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/optimized_tensorflow_runtime/t5x_base_optimized_online_prediction.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/optimized_tensorflow_runtime/bert_optimized_online_prediction.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/vertex_endpoints/optimized_tensorflow_runtime/t5x_base_optimized_online_prediction.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/optimized_tensorflow_runtime/bert_optimized_online_prediction.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/vertex_endpoints/optimized_tensorflow_runtime/t5x_base_optimized_online_prediction.ipynb\">\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/vertex_endpoints/optimized_tensorflow_runtime/bert_optimized_online_prediction.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",

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/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/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_monitoring/get_started_with_model_monitoring_automl.ipynb

@@ -31,7 +31,7 @@
         "id": "fsv4jGuU89rX"
       },
       "source": [
-        "# Vertex AI Model Monitoring for AutoML tabular models\n",
+        "# E2E ML on GCP: MLOps stage 7 : monitoring: Vertex AI Model Monitoring for AutoML tabular models\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -51,7 +51,7 @@
         "      Open in Vertex AI Workbench\n",
         "    </a>\n",
         "  </td>\n",
-        "</table>\n"
+        "</table>"
       ]
     },
     {
@@ -91,6 +91,7 @@
         "- Deploy the `Model` resource to the `Endpoint` resource.\n",
         "- Configure the `Endpoint` resource for model monitoring.\n",
         "- Generate synthetic prediction requests for skew.\n",
+        "- Wait for email alert notification.\n",
         "- Generate synthetic prediction requests for drift.\n",
         "- Wait for email alert notification.\n",
         "\n",
@@ -105,7 +106,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). In this notebook, you use only the fields year, month and day from the dataset to predict the value of mean daily temperature (mean_temp)."
+        "The dataset used for this tutorial is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of this dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp)."
       ]
     },
     {
@@ -562,7 +563,7 @@
       "source": [
         "### Create BigQuery client\n",
         "\n",
-        "In this tutorial, you explore the monitoring data stored in BigQuery. You create a client interface, which you subsequently use to access the data."
+        "In this tutorial, you use data from the same public BigQuery table that was used to train the pre-trained model. You create a client interface, which you subsequently use to access the data."
       ]
     },
     {
@@ -667,6 +668,23 @@
         "print(dataset.resource_name)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "set_transformations:gsod"
+      },
+      "outputs": [],
+      "source": [
+        "TRANSFORMATIONS = [\n",
+        "    {\"auto\": {\"column_name\": \"year\"}},\n",
+        "    {\"auto\": {\"column_name\": \"month\"}},\n",
+        "    {\"auto\": {\"column_name\": \"day\"}},\n",
+        "]\n",
+        "\n",
+        "label_column = \"mean_temp\""
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -685,7 +703,7 @@
         "- `optimization_prediction_type`: The type task to train the model for.\n",
         "  - `classification`: A tabuar classification model.\n",
         "  - `regression`: A tabular regression model.\n",
-        "- `column_transformations`: (Optional): Transformations to apply to the input columns. In this example, you set the column transformations to use the default transformation based on their data type.\n",
+        "- `column_transformations`: (Optional): Transformations to apply to the input columns\n",
         "- `optimization_objective`: The optimization objective to minimize or maximize.\n",
         "  - binary classification:\n",
         "    - `minimize-log-loss`\n",
@@ -701,23 +719,6 @@
         "    - `minimize-rmsle`"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "set_transformations:gsod"
-      },
-      "outputs": [],
-      "source": [
-        "TRANSFORMATIONS = [\n",
-        "    {\"auto\": {\"column_name\": \"year\"}},\n",
-        "    {\"auto\": {\"column_name\": \"month\"}},\n",
-        "    {\"auto\": {\"column_name\": \"day\"}},\n",
-        "]\n",
-        "\n",
-        "label_column = \"mean_temp\""
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1064,9 +1065,7 @@
         "You are receiving this mail because you are using the Vertex AI Model Monitoring service.\n",
         "This mail is to inform you that we received your request to set up drift or skew detection for the Prediction Endpoint listed below. Starting from now, incoming prediction requests will be sampled and logged for analysis.\n",
         "Raw requests and responses will be collected from prediction service and saved in bq://[your-project-id].model_deployment_monitoring_[endpoint-id].serving_predict .\n",
-        "</blockquote>\n",
-        "\n",
-        "*Note:* You do not need to wait for the email notification to continue to the next step."
+        "</blockquote>"
       ]
     },
     {

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/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/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/training/get_started_with_vertex_distributed_training.ipynb

@@ -61,7 +61,8 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This tutorial demonstrates how to use the Vertex AI Python client library to do distrbuted training of a TensorFlow model.\n",
+        "\n",
+        "This tutorial demonstrates how to use Vertex in production. This tutorial covers get started with Vertex Distributed Training. \n",
         "\n",
         "*Note:* There are incompatibilities between Colab and Docker and the Docker section may not work until resolved by the platform.\n",
         "\n",
@@ -76,7 +77,7 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to use `Vertex AI Distributed Training` when training with `Vertex AI`.\n",
+        "In this tutorial, you learn how to use `Vertex AI Distributed Training` for when training with `Vertex AI`.\n",
         "\n",
         "This tutorial uses the following Google Cloud ML services:\n",
         "\n",

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)