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notebooks/official/README.md

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 # Google Cloud Vertex AI Official Notebooks
 
 The official notebooks are organized by Google Cloud Vertex AI products.
+
+The official notebooks are a collection of curated and non-curated notebooks authored by Google Cloud staff members. The curated notebooks are linked to in the [Vertex AI online web documentation](https://cloud.google.com/vertex-ai/docs/tutorials/jupyter-notebooks).
+
+The notebooks are organized into subfolders by Cloud AI services.
+
+## Manifest of Curated Notebooks
+
+### AutoML
+
+[AutoML text classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-text-classification.ipynb)
+
+[AutoML tabular forecasting model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
+
+<blockquote>
+In this tutorial, you create an AutoML tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK. 
+
+This tutorial uses the following Google Cloud ML services:
+
+- AutoML Training
+- Vertex AI Batch Prediction
+- Vertex AI Model resource
+
+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.
+</blockquote>
+
+### Vertex AI Training
+
+[Custom image classification model training and batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)
+
+In this tutorial, you 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.
+
+create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then do a prediction on the deployed model by sending data. 
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Training
+- Vertex AI Batch Prediction
+- Vertex AI Model resource
+
+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.
+
+[Custom image classification model training and online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-online.ipynb)
+
+In this tutorial, you learn to use Vertex AI Training to create a custom-trained model from a Python script in a Docker container, and learn to use Vertex AI Prediction to do a prediction on the deployed model by sending data. 
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Training
+- Vertex AI Prediction
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+The steps performed include:
+
+- Create a Vertex AI custom job for training a TensorFlow model.
+- Upload the trained model artifacts to a Model resource.
+- Create a serving Endpoint resource.
+- Deploy the Model resource to a serving Endpoint resource.
+- Make a prediction.
+- Undeploy the Model resource.
+
+
+### Vertex Explainable AI
+
+[AutoML tabular binary classification model with batch explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb)
+
+[AutoML tabular binary classification model with online explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb)
+
+In this tutorial, you learn to use AutoML to create a tabular binary classification model from a Python script, and then learn to use Vertex AI Online Prediction to make online predictions with explanations. 
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI AutoML
+- Vertex AI Prediction
+- Vertex Explainable AI
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+The steps performed include:
+
+- Create a Vertex Dataset resource.
+- Train an AutoML tabular binary classification model.
+- View the model evaluation metrics for the trained model.
+- Create a serving Endpoint resource.
+- Deploy the Model resource to a serving Endpoint resource.
+- Make an online prediction request with explainability.
+- Undeploy the Model resource.
+
+[Custom tabular regression model with batch explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)
+
+In this tutorial, you 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. 
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Training
+- Vertex AI Batch Prediction
+- Vertex Explainable AI
+- Vertex AI Model resource
+
+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.
+
+[Custom tabular regression model with online explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb)
+
+In this tutorial, you 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 Prediction to make an online prediction request with explanations.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Training
+- Vertex AI Prediction
+- Vertex Explainable AI
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+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.
+- Create a serving Endpoint resource.
+- Deploy the Model resource to a serving Endpoint resource.
+- Make a prediction with explanation.
+- Undeploy the Model resource.
+
+[Custom image classification model with batch predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
+
+[Custom image classification model with online predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb)
+
+### Vertex Feature Store
+
+[Managing features in a feature store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/gapic-feature-store.ipynb)
+
+### Vertex Model Monitoring
+
+[Monitoring drift detection in online serving](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb)
+
+In this notebook, you learn to use the Vertex AI Model Monitoring service to detect drift and anomalies in prediction requests from a deployed Vertex AI Model resource.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Model Monitoring
+- Vertex AI Prediction
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+The steps performed include:
+
+- Upload a pre-trained model as a Vertex AI Model resource.
+- Create an Vertex AI Endpoint resource.
+- Deploy the Model resource to the Endpoint resource.
+- Configure the Endpoint resource for model monitoring.
+- Generate synthetic prediction requests.
+- Understand how to interpret the statistics, visualizations, other data reported by the model monitoring feature.
+
+### Vertex ML Metadata
+
+[Tracking hyperparameters and metrics in custom training job](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
+
+[Tracking hyperparameters and metrics in locally trained job](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
+
+In this notebook, you learn how to use Vertex ML Metadata to track training parameters and evaluation metrics.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex ML Metadata
+- Vertex AI Experiments
+
+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.
+
+### Vertex AI Pipelines
+
+[Creating Python function KFP components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
+
+In this tutorial, you 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.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Pipelines
+
+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
+
+[AutoML image classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
+
+In this tutorial, you learn to use Vertex AI Pipelines and Google Cloud Pipeline Components to build an AutoML image classification model.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Pipelines
+- Google Cloud Pipeline Components
+- Vertex AutoML
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a Dataset 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
+
+[AutoML tabular classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)
+
+In this tutorial, you learn to use Vertex AI Pipelines and Google Cloud Pipeline Components to build an AutoML tabular classification model.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Pipelines
+- Google Cloud Pipeline Components
+- Vertex AutoML
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a Dataset resource.
+    - Train an AutoML tabular 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
+
+[AutoML tabular regression model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
+
+In this tutorial, you learn to use Vertex AI Pipelines and Google Cloud Pipeline Components to build an AutoML tabular regression model.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Pipelines
+- Google Cloud Pipeline Components
+- Vertex AutoML
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+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
+
+[AutoML text classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
+
+[Custom training and batch prediction using prebuilt components pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)
+
+[Custom training using prebuilt and custom components pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb)
+
+In this tutorial, you learn to use Vertex AI Pipelines and Google Cloud Pipeline Components to build and deploy a custom model.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Pipelines
+- Google Cloud Pipeline Components
+- Vertex AI Training
+- Vertex AI Model resource
+- Vertex AI Endpoint resource
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Train a custom model.
+    - Uploads the trained model as a Model resource.
+    - Creates an Endpoint resource.
+    - Deploys the Model resource to the Endpoint resource.
+- Compile the KFP pipeline.
+- Execute the KFP pipeline using Vertex AI Pipelines
+
+[Introduction to control flow in pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)
+
+In this tutorial, you use the KFP SDK to build pipelines that use loops and conditionals, including nested examples.
+
+This tutorial uses the following Google Cloud ML services:
+
+- Vertex AI Pipelines
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Use control flow components
+- Compile the KFP pipeline.
+- Execute the KFP pipeline using Vertex AI Pipelines
+
+[Introduction to KFP components and pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
+
+### Vertex AI Vizier
+
+[Using Vizier for multi-objective study](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb)
+
+
+
+