update indices

notebooks/official/automl/README.md

@@ -49,8 +49,8 @@ The steps performed are:
 - Make a batch prediction with the BigQuery ML model.
 - Create a Vertex AI `Dataset` resource.
 - Train the Vertex AI Forecasting model.
-- View the Model evaluation.
-- Make a batch prediction with the Model.
+- View the Vertex AI Model Evaluation results.
+- Make a batch prediction with the Vertex AI Forecasting model.
 
 ```
 

notebooks/official/bigquery_ml/README.md

@@ -2,21 +2,19 @@
 [Online prediction with BigQuery ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb)
 
 ```
-Learn how to train and deploy a churn prediction model for real-time inference, with the data in BigQuery and model trained using BigQuery ML, registered to Vertex AI Model Registry, and deployed to an endpoint on Vertex AI for online predictions.
+In this tutorial, you fetch the required data from a public BigQuery dataset and prepare it for training.
 
 The steps performed include:
 
-- Using Python & SQL to query the public data in BigQuery
-- Preparing the data for modeling
-- Training a classification model using BigQuery ML and registering it to Vertex AI Model Registry
-- Inspecting the model on Vertex AI Model Registry
-- Deploying the model to an endpoint on Vertex AI
-- Making sample online predictions to the model endpoint
+- Query and fetch the data from the public BigQuery dataset.
+- Prepare the data for training.
+- Train a churn classification model using BigQuery ML.
+- Save the trained model to Vertex AI Model Registry.
+- Deploy the model to a Vertex AI Endpoint.
+- Make online prediction requests to the endpoint.
 
 ```
 
-   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)
 

notebooks/official/custom/README.md

@@ -93,7 +93,7 @@ The steps performed include:
    Learn more about [Vertex AI TensorBoard Profiler](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-profiler).
 
 
-[Get started with Vertex AI Training for XGBoost](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/get_started_vertex_training.ipynb)
+[Get started with Vertex AI Training for XGBoost](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/get_started_vertex_training_xgboost.ipynb)
 
 ```
 Learn how to use `Vertex AI Training` for training a XGBoost custom model.

notebooks/official/experiments/README.md

@@ -74,6 +74,22 @@ The steps performed include:
    Learn more about [Vertex AI TensorBoard](https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview).
 
 
+[Custom training autologging - Local script](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_custom_training_autologging_local_script.ipynb)
+
+```
+Learn how to autolog paramenters and metrics of an ML experiment running on Vertex AI training by leveraging the integration with Vertex AI Experiments.
+
+The steps performed include:
+
+- Formalize model experiment in a script
+- Run model traning using local script on Vertex AI Training
+- Check out ML experiment parameters and metrics in Vertex AI Experiments
+
+```
+
+   Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
+
+
 [Get started with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments.ipynb)
 
 ```
@@ -107,7 +123,7 @@ The steps performed include:
    Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
 
 
-[Autologging](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/autologging.ipynb)
+[Autologging](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/get_started_with_vertex_experiments_autologging.ipynb)
 
 ```
 Learn how to use `Vertex AI Autologging`.

notebooks/official/explainable_ai/README.md

@@ -169,3 +169,20 @@ The steps performed include:
 
    Learn more about [Vertex AI Prediction](https://cloud.google.com/vertex-ai/docs/predictions/get-predictions).
 
+
+[Explaining text classification with Vertex Explainable AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/xai_text_classification_feature_attributions.ipynb)
+
+```
+Learn how to configure feature-based explanations using **sampled Shapley method** on a TensorFlow text classification model for online predictions with explanations.
+
+The steps performed include:
+
+- Build and train a TensorFlow text classification model
+- Upload model for deployment
+- Deploy model for online prediction
+- Make online prediction with explanations
+
+```
+
+   Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
+

notebooks/official/feature_store/README.md

@@ -15,6 +15,38 @@ The steps performed include:
    Learn more about [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore).
 
 
+[Online feature serving and fetching of BigQuery data with Vertex AI Feature Store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/online_feature_serving_and_fetching_bigquery_data_with_feature_store.ipynb)
+
+```
+Learn how to create and use an online feature store instance to host and serve data in `BigQuery` with `Vertex AI Feature Store` in an end to end workflow of feature values serving and fetching user journey.
+
+The steps performed include:
+
+- Provision an online feature store instance to host and serve data.
+- Register a `BigQuery` view with the online feature store instance and set up the sync job.
+- Use the online server to fetch feature values for online prediction.
+
+```
+
+   Learn more about [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore/overview).
+
+
+[Online feature serving and vector retrieval of BigQuery data with Vertex AI Feature Store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/online_feature_serving_and_vector_retrieval_bigquery_data_with_feature_store.ipynb)
+
+```
+Learn how to create and use an online feature store instance to host and serve data in `BigQuery` with `Vertex AI Feature Store` in an end to end workflow of features serving and vector retrieval user journey.
+
+The steps performed include:
+
+- Provision an online feature store instance to host and serve data.
+- Create an online feature store instance to serve a `BigQuery` table.
+- Use the online server to search nearest neighbors.
+
+```
+
+   Learn more about [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore/overview).
+
+
 [Using Vertex AI Feature Store with Pandas Dataframe](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb)
 
 ```
@@ -22,7 +54,8 @@ Learn how to use `Vertex AI Feature Store` with pandas Dataframe.
 
 The steps performed include:
 
-- Ingest Feature values from Pandas DataFrame into Feature Store's Entity types.
+- Create Featurestore, entity types and features.
+- Ingest feature values from Pandas DataFrame into Feature Store's Entity types.
 - Read Entity feature values from Online Feature Store into Pandas DataFrame.
 - Batch serve feature values from your Feature Store into Pandas DataFrame.
 

notebooks/official/generative_ai/README.md

@@ -0,0 +1,93 @@
+
+[Vertex AI LLM Evaluation & Batch Inference](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/batch_eval_llm.ipynb)
+
+```
+Learn to use Vertex AI to evaluate a large language model.
+
+The steps performed include:
+
+- Create Vertex AI Pipeline job using a predefined template for bulk inference.
+- Execute the pipeline using Vertex AI Pipelines.
+- Produce prediction results against a model for a given dataset.
+
+```
+
+   Learn more about [Overview of Generative AI support on Vertex AI](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview).
+
+
+[Vertex AI LLM Reinforcement Learning from Human Feedback](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/genertive_ai/rlhf_tune_llm.ipynb)
+
+```
+In this tutorial, you will use `Vertex AI RLHF` to tune and deploy a large language model model.
+
+The steps performed include:
+
+- Set the number of model tuning steps.
+- Create Vertex AI Pipeline job using a predefined template for tuning.
+- Execute the pipeline using `Vertex AI Pipelines`.
+- Perform online prediction with the tuned model.
+
+```
+
+
+[Semantic Search using Embeddings](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/text_embedding_api_semantic_search_with_scann.ipynb)
+
+```
+In this tutorial, we demonstrate how to create an embedding generated from text and perform a semantic search.
+
+The steps performed include:
+- Installation and imports
+- Create embedding dataset
+- Create an index
+- Query the index
+
+```
+
+   Learn more about [text embedding](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
+
+
+[Text Embedding New API](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/text_embedding_new_api.ipynb)
+
+```
+Learn how to call text embedding latest APIs on two
+new models, textembedding-gecko@latest and textembedding-gecko-multilingual@latest:
+
+The steps performed include:
+
+- Installation and imports
+- Generate embeddings
+
+```
+
+   Learn more about [text embedding api](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings#api_changes_to_models_released_in_or_after_august_2023).
+
+
+[Vertex AI Tuning a PEFT model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/tune_peft.ipynb)
+
+```
+Learn to use `Vertex AI LLM` to tune and deploy a PEFT large language model.
+
+The steps performed include:
+
+- Get the Vertex AI LLM model.
+- Tune the model.
+  - This will automatically create a Vertex AI endpoint and deploy the model to it.
+- Make a prediction using `Vertex AI LLM`.
+- Make a prediction using `Vertex AI Prediction`
+
+```
+
+
+[Using the Vertex AI SDK with Large Language Models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/generative_ai/vertex_sdk_llm_snippets.ipynb)
+
+```
+Learn how to provide text input to Large Language Models  available on Vertex AI to test, tune, and deploy generative AI language models.
+
+The steps performed include:
+
+- Use the predict endpoints of Vertex AI PaLM API to receive generative AI responses to a message.
+- Use the text embedding endpoint to receive a vector representation of a message.
+- Perform prompt tuning of an LLM, based on input/output training data.
+
+```
+

notebooks/official/matching_engine/README.md

@@ -1,4 +1,21 @@
 
+[Using Vertex AI Multimodal Embeddings and Vector Search](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_create_multimodal_embeddings.ipynb)
+
+```
+Learn how to encode custom text embeddings, create an  Approximate Nearest Neighbor  index, and query against indexes.
+
+The steps performed include:
+
+* Convert an image dataset to embeddings
+* Create an index
+* Upload embeddings to the index
+* Create an index endpoint
+* Deploy the index to the index endpoint
+* Perform an online query
+
+```
+
+
 [Using Vertex AI Matching Engine for StackOverflow Questions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_create_stack_overflow_embeddings.ipynb)
 
 ```
@@ -16,6 +33,27 @@ The steps performed include:
    Learn more about [Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview).
 
 
+[Using Vertex AI Vector Search and Vertex AI Embeddings for Text for StackOverflow Questions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_create_stack_overflow_embeddings_vertex.ipynb)
+
+```
+Learn how to encode text embeddings, create an Approximate Nearest Neighbor  index, and query against indexes.
+
+The steps performed include:
+
+* Convert a BigQuery dataset to embeddings
+* Create an index
+* Upload embeddings to the index
+* Create an index endpoint
+* Deploy the index to the index endpoint
+* Perform an online query
+
+```
+
+   Learn more about [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview).
+
+   Learn more about [Vertex AI Embeddings for Text](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
+
+
 [Using Vertex AI Matching Engine for Text-to-Image Embeddings](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_create_text_to_image_embeddings.ipynb)
 
 ```
@@ -33,6 +71,23 @@ The steps performed include:
    Learn more about [Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview).
 
 
+[Using Vertex AI Vector Search for Text-to-Image Embeddings](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_create_text_to_image_embeddings.ipynb)
+
+```
+Learn how to encode custom text embeddings, create an  Approximate Nearest Neighbor  index, and query against indexes.
+
+The steps performed include:
+
+* Create ANN index
+* Create an index endpoint with VPC Network
+* Deploy ANN index
+* Perform online query
+
+```
+
+   Learn more about [Vertex AI Vector Search](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)
 
 ```

notebooks/official/migration/README.md

@@ -37,7 +37,7 @@ 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).
 
 
-[AutoML Video Object Tracking](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb)
+[AutoML Video Object Tracking](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-object-tracking-batch-prediction.ipynb)
 
 ```
 Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.
@@ -119,7 +119,7 @@ The steps performed include the following:
    Learn more about [Entity extraction for text data](https://cloud.google.com/vertex-ai/docs/training-overview#entity_extraction_for_text).
 
 
-[AutoML Text Sentiment Analysis](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb)
+[AutoML Text Sentiment Analysis](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-text-sentiment-analysis-batch-prediction.ipynb)
 
 ```
 The objective of this notebook is to build a AutoML Text Sentiment Analysis model.
@@ -140,7 +140,7 @@ The steps performed include the following:
    Learn more about [Sentiment analysis for text data](https://cloud.google.com/vertex-ai/docs/training-overview#sentiment_analysis_for_text).
 
 
-[AutoML Video Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb)
+[AutoML Video Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-automl-video-classification-batch-prediction.ipynb)
 
 ```
 Learn to use `AutoML` to train a video model and use `Vertex AI Batch Prediction` to do batch predictions.

notebooks/official/ml_metadata/README.md

@@ -14,6 +14,8 @@ The steps performed include:
 
    Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
 
+   Learn more about [Vertex AI Experiments](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments).
+
 
 [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)
 

notebooks/official/model_evaluation/README.md

@@ -45,22 +45,22 @@ The steps performed include:
 [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)
 
 ```
-Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.
+Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build and evaluate an `AutoML` text classification model.
 
 The steps performed include:
 
 - Create a Vertex AI `Dataset`.
-- Train a Automl Text Classification model on the `Dataset` resource.
+- Train an Automl Text Classification model on the `Dataset` resource.
 - Import the trained `AutoML model resource` into the pipeline.
 - Run a `Batch Prediction` job.
-- Evaulate the AutoML model using the `Classification Evaluation Component`.
-- Import the classification metrics to the AutoML model resource.
+- Evaluate the AutoML model using the `Classification Evaluation Component`.
+- Import the evaluation metrics to the AutoML model resource.
 
 ```
 
    Learn more about [Vertex AI Model Evaluation](https://cloud.google.com/vertex-ai/docs/evaluation/introduction).
 
-   Learn more about [AutoML Text](https://cloud.google.com/vertex-ai/docs/text-data/classification/prepare-data).
+   Learn more about [Classification on text data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_text).
 
 
 [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)
@@ -84,7 +84,7 @@ The steps performed include:
    Learn more about [Classification for video data](https://cloud.google.com/vertex-ai/docs/training-overview#classification_for_videos).
 
 
-[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)
+[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)
 
 ```
 In this tutorial, you train a scikit-learn RandomForest model, save it in Vertex AI Model Registry and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`.

notebooks/official/pipelines/README.md

@@ -240,26 +240,6 @@ The steps performed include:
    Learn more about [BigQuery ML components](https://cloud.google.com/vertex-ai/docs/pipelines/bigqueryml-component).
 
 
-[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)
-
-```
-Learn how to build a Vertex AI pipeline and train a random-forest model using Spark ML for loan-eligibility classification problem.
-
-The steps performed include:
-
-*   Use the `DataprocPySparkBatchOp` to preprocess data.
-*   Create a Vertex AI dataset resource on the training data.
-*   Train a random forest model using PySpark.
-*   Build a Vertex AI pipeline and run the training job.
-*   Use the Spark serving image in order to deploy a Spark model on Vertex AI Endpoint.
-
-```
-
-   Learn more about [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction).
-
-   Learn more about [Dataproc components](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component).
-
-
 [Model train, upload, and deploy using Google Cloud Pipeline Components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb)
 
 ```
@@ -301,6 +281,25 @@ The steps performed include:
    Learn more about [Vertex AI Model components](https://cloud.google.com/vertex-ai/docs/pipelines/model-endpoint-component).
 
 
+[Vertex AI Pipelines with KFP 2.x](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/kfp2_pipeline.ipynb)
+
+```
+Learn to use `Vertex AI Pipelines` and KFP 2.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a `BigQuery Dataset` resource.
+    - Export the dataset.
+    - Train an XGBoost `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`
+
+```
+
+
 [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)
 
 ```

notebooks/official/prediction/README.md

@@ -15,6 +15,61 @@ The steps performed include:
    Learn more about [Vertex AI Batch Prediction](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/get-batch-predictions).
 
 
+[Get started with Custom Prediction Routine (CPR)](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/get_started_with_cpr.ipynb)
+
+```
+Learn how to use Custom Prediction Routine  for `Vertex AI Predictions`.
+
+The steps performed include:
+
+- Write a custom data preprocessor.
+- Train the model.
+- Build a custom scikit-learn serving container with custom data preprocessing using the Custom Prediction Routine model server.
+    - Test the model serving container locally.
+    - Upload and deploy the model serving container to Vertex AI Endpoint.
+    - Make a prediction request.
+- Build a custom scikit-learn serving container with custom predictor (post-processing) using the Custom Prediction Routine model server.
+    - Implement custom predictor.
+    - Test the model serving container locally.
+    - Upload and deploy the model serving container to Vertex AI Endpoint.
+    - Make a prediction request.
+- Build a custom scikit-learn serving container with custom predictor and HTTP request handler using the Custom Prediction Routine model server.
+    - Implement a custom handler.
+    - Test the model serving container locally.
+    - Upload and deploy the model serving container to Vertex AI Endpoint.
+    - Make a prediction request.
+- Customize the Dockerfile for a custom scikit-learn serving container with custom predictor and HTTP request handler using the Custom Prediction Routine model server.
+    - Implement a custom Dockerfile.
+    - Test the model serving container locally.
+    - Upload and deploy the model serving container to Vertex AI Endpoint.
+    - Make a prediction request.
+
+```
+
+   Learn more about [Custom prediction routines](https://cloud.google.com/vertex-ai/docs/predictions/custom-prediction-routines).
+
+
+[Vertex AI LLM and streaming prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/llm_streaming_prediction.ipynb)
+
+```
+Learn how to use Vertex AI LLM to download pretrained LLM model, make predictions and finetuning the model.
+
+The steps performed include:
+
+- Load a pretrained text generation model.
+- Make a non-streaming prediction
+- Load a pretrained text generation model, which supports streaming.
+- Make a streaming prediction
+- Load a pretrained chat model.
+- Do a local interactive chat session.
+- Do a batch prediction with a text generation model.
+- Do a batch prediction with a text embedding model.
+
+```
+
+   Learn more about [Vertex AI Language Models](https://cloud.google.com/python/docs/reference/aiplatform/latest/vertexai.language_models.TextGenerationModel#vertexai_language_models_TextGenerationModel_predict_streaming).
+
+
 [Serving PyTorch image models with prebuilt containers on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/pytorch_image_classification_with_prebuilt_serving_containers.ipynb)
 
 ```
@@ -51,3 +106,21 @@ The steps performed include:
 
 ```
 
+
+[Vertex AI SDK 2.0 Vertex AI Remote Prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/prediction/sdk2_remote_prediction.ipynb)
+
+```
+Learn to use `Vertex AI SDK 2.
+
+The steps performed include:
+
+- Download and split the dataset
+- Perform transformations as a Vertex AI remote training.
+- For scikit-learn, PyTorch, TensorFlow, PyTorch Lightning
+    - Train the model remotely.
+    - Uptrain the pretrained model remotely.
+    - Evaluate both the pretrained and uptrained model.
+    - Make a prediction remotely
+
+```
+

notebooks/official/sdk/README.md

@@ -40,3 +40,51 @@ The steps performed include:
 
    Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
 
+
+[Train a pytorch model with Vertex AI SDK 2.0 and Bigframes](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/sdk2_bigframes_pytorch.ipynb)
+
+```
+Learn to use `Vertex AI SDK 2.
+
+The steps performed include:
+
+- Initialize a dataframe from a BigQuery table and split the dataset
+- Perform transformations as a Vertex AI remote training.
+- Train the model remotely and evaluate the model locally
+
+```
+
+   Learn more about [bigframes](https://cloud.google.com/bigquery/docs/).
+
+
+[Train a scikit-learn model with Vertex AI SDK 2.0 and Bigframes](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/sdk2_bigframes_sklearn.ipynb)
+
+```
+Learn to use `Vertex AI SDK 2.
+
+The steps performed include:
+
+- Initialize a dataframe from a BigQuery table and split the dataset
+- Perform transformations as a Vertex AI remote training.
+- Train the model remotely and evaluate the model locally
+
+```
+
+   Learn more about [bigframes](https://cloud.google.com/bigquery/docs/).
+
+
+[Train a Tensorflow Keras model with Vertex AI SDK 2.0 and Bigframes](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/sdk/sdk2_bigframes_tensorflow.ipynb)
+
+```
+Learn to use `Vertex AI SDK 2.
+
+The steps performed include:
+
+- Initialize a dataframe from a BigQuery table and split the dataset
+- Perform transformations as a Vertex AI remote training.
+- Train the model remotely and evaluate the model locally
+
+```
+
+   Learn more about [bigframes](https://cloud.google.com/bigquery/docs/).
+

notebooks/official/tabnet/README.md

@@ -35,6 +35,21 @@ The steps performed include:
    Learn more about [Tabular Workflow for TabNet](https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/tabnet).
 
 
+[Train a TabNet model using Vertex AI Remote Training with Vertex AI SDK 2.0](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/sdk2_remote_tabnet_training.ipynb)
+
+```
+Learn to use `Vertex AI SDK 2.
+
+The steps performed include:
+
+- Download and split the dataset
+- Ingest the data in a Dataframe and perform transformations.
+- Train a tabular classification model.
+- Train a tabular regression model.
+
+```
+
+
 [Vertex AI TabNet](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabnet/tabnet_vertex_tutorial.ipynb)
 
 ```

notebooks/official/tabular_workflows/README.md

@@ -20,7 +20,7 @@ The steps performed are:
 [TabNet Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb)
 
 ```
-Learn how to create two classification models using Vertex AI TabNet Tabular Workflows.
+Learn how to create classification models on tabular data using two of the Vertex AI TabNet Tabular Workflows.
 
 The steps performed include:
 

notebooks/official/training/README.md

@@ -1,4 +1,20 @@
 
+[Distributed Vertex AI Hyperparameter Tuning](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/distributed_hyperparameter_tuning.ipynb)
+
+```
+In this notebook, you create a custom trained model from a Python script in a Docker container.
+
+The steps performed include:
+
+- Training using a Python package.
+- Report accuracy when hyperparameter tuning.
+- Save the model artifacts to Cloud Storage using GCSFuse.
+
+```
+
+   Learn more about [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
+
+
 [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)
 
 ```
@@ -33,6 +49,23 @@ The steps performed include:
    Learn more about [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
 
 
+[Vertex AI Hyperparameter Tuning for XGBoost](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/hyperparameter_tuning_xgboost.ipynb)
+
+```
+Learn how to use `Vertex AI Hyperparameter Tuning` for training a XGBoost custom model.
+
+The steps performed include:
+
+- Training using a Python package.
+- Report accuracy when hyperparameter tuning.
+- Save the model artifacts to Cloud Storage using GCSFuse.
+- Create a `Vertex AI Model` resource.
+
+```
+
+   Learn more about [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview).
+
+
 [PyTorch image classification multi-node distributed data parallel training on cpu using Vertex training with custom container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb)
 
 ```
@@ -43,7 +76,7 @@ The steps performed include:
 - Setting up your GCP project : Setting up the PROJECT_ID, REGION & SERVICE_ACCOUNT
 - Creating a cloud storage bucket
 - Building Custom Container using Artifact Registry and Docker
-- Create a Vertex AI tensorboard instance to store your Vertex AI experiment
+- Create a Vertex AI TensorBoard instance to store your Vertex AI experiment
 - Run a Vertex AI SDK CustomContainerTrainingJob
 
 ```
@@ -51,22 +84,20 @@ The steps performed include:
    Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
 
 
-[PyTorch Image Classification Multi-Node Distributed Data Parallel Training on GPU using Vertex AI Training with Custom Container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb)
+[PyTorch image classification multi-node NCCL distributed data parallel training on cpu using Vertex training with custom container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb)
 
 ```
 Learn how to create a distributed PyTorch training job using Vertex AI SDK for Python and custom containers.
 
 The steps performed include:
 
-- Setting up your GCP project : Setting up the PROJECT_ID, REGION & SERVICE_ACCOUNT
-- Creating a cloud storage bucket
 - Building Custom Container using Artifact Registry and Docker
-- Create a Vertex AI Tensorboard Instance to store your Vertex AI experiment
+- Create a Vertex AI tensorboard instance to store your Vertex AI experiment
 - Run a Vertex AI SDK CustomContainerTrainingJob
 
 ```
 
-   Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
+   Learn more about [Vertex AI Training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
 
 
 [Training, tuning and deploying a PyTorch text sentiment classification model on Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/pytorch-text-sentiment-classification-custom-train-deploy.ipynb)
@@ -89,10 +120,58 @@ The steps performed include:
    Learn more about [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training).
 
 
-[Create a distributed custom training job](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.ipynb)
+[Train PyTorch model on Vertex AI with data from Cloud Storage](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/pytorch_gcs_data_training.ipynb)
 
 ```
-Learn how to create a distributed training job using Vertex AI SDK for Python.
+Learn how to create a training job using PyTorch and a dataset stored on Cloud Storage.
+
+The steps performed include:
+
+- Write a custom training script that creates your train & test datasets and trains the model.
+- Run a Vertex AI SDK `CustomTrainingJob`
+
+```
+
+   Learn more about [PyTorch integration in Vertex AI](https://cloud.google.com/vertex-ai/docs/start/pytorch).
+
+
+[Vertex AI SDK 2.0 Vertex AI Remote Hyperparameter Tuning for OSS ML frameworks](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/sdk2_remote_hyperparameter_tuning.ipynb)
+
+```
+Learn to use `Vertex AI SDK 2.
+
+The steps performed include:
+
+- Download and split the dataset
+- Perform transformations as a Vertex AI remote training.
+- For scikit-learn, PyTorch, TensorFlow, PyTorch Lightning, Tabnet
+    - Tune the model remotely.
+    - Get the best model.
+
+```
+
+
+[Vertex AI SDK 2.0 Vertex AI Remote Training for OSS ML frameworks](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/sdk2_remote_training.ipynb)
+
+```
+Learn to use `Vertex AI SDK 2.
+
+The steps performed include:
+
+- Download and split the dataset
+- Perform transformations as a Vertex AI remote training.
+- For scikit-learn, PyTorch, TensorFlow, PyTorch Lightning
+    - Train the model remotely.
+    - Uptrain the pretrained model remotely.
+    - Evaluate both the pretrained and uptrained model.
+
+```
+
+
+[Distributed XGBoost training with Dask](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/xgboost_data_parallel_training_on_cpu_using_dask.ipynb)
+
+```
+Learn how to create a distributed training job using XGBoost with Dask.
 
 The steps performed include:
 

notebooks/official/workbench/README.md

@@ -147,10 +147,6 @@ The steps performed include:
 
 ```
 
-   Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-   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)
 
@@ -242,7 +238,7 @@ The steps performed are:
 
    Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
 
-   Learn more about [Dataproc](https://cloud.google.com/vertex-ai/docs/pipelines/dataproc-component).
+   Learn more about [Dataproc Serverless for Spark](https://cloud.google.com/dataproc-serverless/docs/guides/bigquery-connector-spark-example).
 
 
 [SparkML with Dataproc and BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/workbench/spark/spark_ml.ipynb)
@@ -271,28 +267,3 @@ The steps performed are:
 
    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
-
-```
-
-   Learn more about [Vertex AI Workbench](https://cloud.google.com/vertex-ai/docs/workbench/introduction).
-
-   Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview).
-