Vertex AI notebook tutorials

Tutorial steps

• Create a Vertex AI model training job.
• Train an AutoML Tabular model.
• Deploy the model resource to a serving endpoint resource.
• Make a prediction by sending data.
• Undeploy the model resource.

Tutorial steps

• Create a Vertex dataset resource.
• Train the model.
• View the model evaluation.
• Make a batch prediction.

Tutorial steps

• Create a Vertex Dataset resource.
• Train the model.
• View the model evaluation.
• Deploy the Model resource to a serving Endpoint resource.
• Make a prediction.
• Undeploy the Model.

Tutorial steps

• Create a Vertex dataset resource.
• Train the model.
• Export the edge model from the model resource to Cloud Storage.
• Download the model locally.
• Make a local prediction.

Tutorial steps

• Create a Vertex AI dataset resource.
• Train the model.
• View the model evaluation.
• Deploy the model resource to a serving endpoint resource.
• Make a prediction.
• Undeploy the model.

Tutorial steps

• Create a training pipeline that reduces the search space from the default to save time.
• Create a training pipeline that reuses the architecture search results from the previous pipeline to save time.

Tutorial steps

• 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

Tutorial steps

• Create a Vertex AI TimeSeriesDataset resource.
• Train the model.
• View the model evaluation.
• Make a batch prediction.

Tutorial steps

• Create a Vertex dataset resource.
• Train the model.
• View the model evaluation.
• Make a batch prediction.

Tutorial steps

• 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.

Tutorial steps

• Create a Vertex AI dataset resource.
• Train an AutoML tabular regression model resource.
• Obtain the evaluation metrics for the model resource.
• Make a batch prediction.

Tutorial steps

• Create a Vertex dataset resource.
• Train the model.
• View the model evaluation.
• Deploy the model resource to a serving Endpoint resource.
• Make a prediction.
• Undeploy the model.

Tutorial steps

• Create a Vertex AI dataset resource.
• Train the model.
• View the model evaluation.
• Make a batch prediction.

Tutorial steps

• Create a Vertex dataset resource.
• Train the model.
• View the model evaluation.
• Make a batch prediction.

Tutorial steps

• Create a Vertex dataset resource.
• Train the model.
• View the model evaluation.
• Make a batch prediction.

Tutorial steps

• 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

Tutorial steps

• Train a model that uses flower's measurements as input to predict the class of iris.
• Save the model and its serialized preprocessor.
• Build a FastAPI server to handle predictions and health checks.
• Build a custom container with model artifacts.
• Upload and deploy custom container to Vertex AI Endpoints.

Tutorial steps

• Create a Vertex AI custom TrainingPipeline for training a model.
• Train a TensorFlow model.
• Deploy the Model resource to a serving Endpoint resource.
• Make a prediction.
• Undeploy the Model resource.

Tutorial steps

• Create a Vertex AI custom job for training a model.
• Train and register a TensorFlow model using a custom container.
• List the registered model in the Vertex AI Model Registry.

Tutorial steps

• Setup a service account and a Cloud Storage bucket
• Create a Vertex AI TensorBoard instance
• Create and run a custom training job
• View the Cloud Profiler dashboard

Tutorial steps

• Training using a Python package.
• Report accuracy when hyperparameter tuning.
• Save the model artifacts to Cloud Storage using Cloud StorageFuse.
• Create a Vertex AI model resource.

Tutorial steps

• Upload a pretrained image classification model as a Model resource (model A).
• Upload a pretrained text sentence encoder model as a Model resource (model B).
• Create a shared VM deployment resource pool.
• List shared VM deployment resource pools.
• Create two Endpoint resources.
• Deploy first model (model A) to first Endpoint resource using deployment resource pool.
• Deploy second model (model B) to second Endpoint resource using deployment resource pool.
• Make a prediction request with first deployed model (model A).
• Make a prediction request with second deployed model (model B).

Tutorial steps

• Create a Vertex AI custom job for training a TensorFlow model.
• Upload the trained model artifacts as a model resource.
• Make a batch prediction.

Tutorial steps

• 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.

Tutorial steps

• Create a Vertex AI dataset resource from BigQuery table compatible for AutoML training.
• Extract a copy of the dataset from BigQuery to a CSV file in Cloud Storage compatible for AutoML or custom training.
• Select rows from a BigQuery dataset into a pandas dataframe compatible for custom training.
• Select rows from a BigQuery dataset into a tf.data.Dataset compatible for custom training TensorFlow models.
• Select rows from extracted CSV files into a tf.data.Dataset compatible for custom training TensorFlow models.
• Create a BigQuery dataset from CSV files.
• Extract data from BigQuery table into a DMatrix compatible for custom training XGBoost models.

Tutorial steps

• Execute module for preprocessing data
• Create a dataset artifact
• Log parameters
• Execute module for training the model
• Log parameters
• Create model artifact
• Assign tracking lineage to dataset, model and parameters

Tutorial steps

• log the model parameters
• log the loss and metrics on every epoch to Vertex AI TensorBoard
• log the evaluation metrics

Tutorial steps

• Formalize a training component
• Build a training pipeline
• Run several pipeline jobs and log their results
• Compare different pipeline jobs

Tutorial steps

• How to delete the TB Experiment with a predefined keyvalue label pair
• How to delete the TB Experiments created before the create_time
• How to delete the TB Experiments created before the update_time

Tutorial steps

• 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

Tutorial steps

• 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

Tutorial steps

• Enable autologging in the Vertex AI SDK.
• Train scikitlearn model and see the resulting experiment run with metrics and parameters autologged to Vertex AI Experiments without setting an experiment run.
• Train Tensorflow model, check autologged metrics and parameters to Vertex AI Experiments by manually setting an experiment run with aiplatform.start_run() and aiplatform.end_run().
• Disable autologging in the Vertex AI SDK, train a PyTorch model and check that none of the parameters or metrics are logged.

Tutorial steps

• Create a Vertex AI managed dataset resource.
• Train an AutoML tabular binary classification model.
• View the model evaluation metrics for the trained model.
• Make a batch prediction request with explainability.

Tutorial steps

• Create a Vertex AI 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.

Tutorial steps

• Create a Vertex AI custom job for training a TensorFlow model.
• View the model evaluation for the trained model.
• Set explanation parameters for when the model is deployed.
• Upload the trained model artifacts and explanation parameters as a Model resource.
• Make a batch prediction with explanations.

Tutorial steps

• 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.

Tutorial steps

• Create a Vertex AI custom job for training a TensorFlow model.
• View the model evaluation for the trained model.
• Set explanation parameters for the model.
• Upload the trained model artifacts as a model resource.
• Make a batch prediction with explanations.

Tutorial steps

• 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.

Tutorial steps

• Create a Vertex AI custom job for training a TensorFLow model.
• Train a TensorFlow model.
• Retrieve and load the model artifacts.
• View the model evaluation for the trained model.
• Set explanation parameters.
• Upload the model as a Vertex AI model resource.
• Deploy the Model resource to a serving endpoint resource.
• Make a prediction with explanation.
• Undeploy the Model resource.

Tutorial steps

• Download pretrained model from TensorFlow Hub
• Upload model for deployment
• Deploy model for online prediction
• Make online prediction with explanations
• Make batch predictions with explanations

Tutorial steps

• Build and train a TensorFlow text classification model
• Upload model for deployment
• Deploy model for online prediction
• Make online prediction with explanations

Tutorial steps

• 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.

Tutorial steps

• Provision an online feature store instance to host and serve data using Optimized online serving with Public or Private endpoint.
• 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.

Tutorial steps

• 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.

Tutorial steps

• 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.

Tutorial steps

• Create a feature view configured to use a dedicated service account.
• A service account is created for each feature view. Such service account is used to sync data from BigQuery.
• Get/List feature view API returns the autocreated service account. Users need to call bq addiampolicybinding command to grant roles/bigquery.dataViewer to the service account.

Tutorial steps

• Create a featurestore.
• Create a new entity type for your featurestore.
• Import feature values from Pandas DataFrame into the entity type in the featurestore.

Tutorial steps

• Create Featurestore, EntityType, and Feature resources.
• Import feature values from Pandas DataFrame into the entity type.
• Read entity feature values from the online feature store into Pandas DataFrame.
• Batch serve feature values from your featurestore into Pandas DataFrame.
• Online serving with updated feature values.
• Pointintime correctness to fetch feature values for training.

Tutorial steps

• Create Featurestore, EntityType, and Feature resources.
• Import feature data into the Featurestore resource.
• Serve online prediction requests using the imported features.
• Access imported features in offline jobs, such as training jobs.
• Use streaming import to import small amount of data.

Tutorial steps

• 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.

Tutorial steps

• Get the Vertex AI LLM model.
• Distill the model(this automatically creates a Vertex AI endpoint and deploys the model to the endpoint).
• Make a prediction using Vertex AI LLM.

Tutorial steps

• Set the number of model tuning steps.
• Create a Vertex AI Pipeline job using a predefined tuning template.
• Execute the pipeline using Vertex AI Pipelines.
• Get predictions from the tuned model.

Tutorial steps

• Installation and imports
• Create embedding dataset
• Create an index
• Query the index

Tutorial steps

• Installation and imports
• Generate embeddings

Tutorial steps

Tutorial steps

• Get the Vertex AI LLM model.
• Tune the model.
• This automatically creates a Vertex AI endpoint and deploy the model to it.
• Make a prediction using Vertex AI LLM.
• Make a prediction using Vertex AI Prediction.

Tutorial steps

Tutorial steps

• 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.

Tutorial steps

• Train an AutoML image classification model.
• Make a batch prediction.
• Deploy model to a endpoint
• Make a online prediction

Tutorial steps

• Train an AutoML object detection model.
• Make a batch prediction.
• Deploy model to a endpoint
• Make a online prediction

Tutorial steps

• Train an AutoML video object tracking model.
• Make a batch prediction.

Tutorial steps

• Create a Vertex AI dataset resource.
• Train the model.
• View the model evaluation.
• Deploy the model resource to a serving endpoint resource.
• Make a prediction.
• Undeploy the model

Tutorial steps

• Train an AutoML video classification model.
• Make a batch prediction.

Tutorial steps

• Package the training code into a python application.
• Containerize the training application using Cloud Build and Artifact Registry.
• Create a custom container training job in Vertex AI and run it.
• Evaluate the model generated from the training job.
• Create a model resource for the trained model in Vertex AI Model Registry.
• Run a Vertex AI batch prediction job.
• Deploy the model resource to a Vertex AI endpoint.
• Run a online prediction job on the model resource.
• Clean up the resources created.

Tutorial steps

• Package the training code into a python application.
• Containerize the training application using Cloud Build and Artifact Registry.
• Create a custom container training job in Vertex AI and run it.
• Evaluate the model generated from the training job.
• Create a model resource for the trained model in Vertex AI Model Registry.
• Run a Vertex AI batch prediction job.
• Deploy the model resource to a Vertex AI endpoint.
• Run a online prediction job on the model resource.
• Clean up the resources created.

Tutorial steps

• Create a Vertex AI custom job for training a scikitlearn model.
• Upload the trained model artifacts as a model resource.
• Generate batch predictions.
• Deploy the model resource to a serving endpoint resource.
• Generate online predictions.
• Undeploy the model resource.

Tutorial steps

• Create a Vertex AI custom job for training a xgboost model.
• Upload the trained model artifacts as a model resource.
• Generate batch predictions.
• Deploy the model resource to a serving endpoint resource.
• Generate online predictions.
• Undeploy the model resource.

Tutorial steps

• Create a Vertex AI hyperparameter tuning job for training a TensorFlow model.

Tutorial steps

• Creating a private Docker repository.
• Tagging a container image, specific to the private Docker repository.
• Pushing a container image to the private Docker repository.
• Pulling a container image from the private Docker repository.
• Deleting a private Docker repository.

Tutorial steps

• Track training parameters and prediction metrics for a custom training job.
• Extract and perform analysis for all parameters and metrics within an Experiment.

Tutorial steps

• Track parameters and metrics for a locally trained model.
• Extract and perform analysis for all parameters and metrics within an experiment.

Tutorial steps

• Use the Kubeflow Pipelines SDK to build an ML pipeline that runs on Vertex AI.
• The pipeline creates a dataset, trains a scikitlearn model, and deploys the model to an endpoint.
• Write custom pipeline components that generate artifacts and metadata.
• Compare Vertex AI Pipeline runs, both in the Google Cloud console and programmatically.
• Trace the lineage for pipelinegenerated artifacts.
• Query your pipeline run metadata.

Tutorial steps

• Create a Vertex AI Dataset.
• Train an Automl Tabular classification model on the Dataset resource.
• Import the trained AutoML model resource into the pipeline.
• Run a Batch Prediction job.
• Evaluate the AutoML model using the Classification Evaluation component.
• Import the classification metrics to the AutoML model resource.

Tutorial steps

• Create a Vertex AI dataset.
• Configure an AutoMLTabularTrainingJob class.
• Run the AutoMLTabularTrainingJob which returns a model.
• Import a pretrained AutoML model resource into the pipeline.
• Run a batch prediction job in the pipeline.
• Evaluate the AutoML model using the regression evaluation component.
• Import the generated regression metrics into the AutoML model resource.

Tutorial steps

• Create a Vertex AI dataset.
• Train a Automl video Classification model on the Vertex AI dataset resource.
• Import the trained AutoML Vertex AI Model resource into the pipeline.
• Run a batch prediction job inside the pipeline.
• Evaluate the AutoML model using the classification evaluation component.
• Import the classification metrics to the AutoML Vertex AI Model resource.

Tutorial steps

• Fetch the dataset from the public source.
• Preprocess the data locally and save test data in BigQuery.
• Train a RandomForest classification model locally using scikitlearn Python package.
• Create a custom container in Artifact Registry for predictions.
• Upload the model in Vertex AI Model Registry.
• Create and run a Vertex AI Pipeline that

Tutorial steps

• Create a Vertex AI Custom Training Job to train a TensorFlow model.
• Run the custom training job.
• Retrieve and load the model artifacts.
• View the model evaluation.
• Upload the model as a Vertex AI model resource.
• Import a pretrained Vertex AI model resource into the pipeline.
• Run a batch prediction job in the pipeline.
• Evaluate the model using the regression evaluation component.
• Import the Regression Metrics to the Vertex AI model resource.

Tutorial steps

• Create a evaluation dataset with predictions and human preference data.
• Preprocess the data locally and save it in Cloud Storage.
• Create and run a Vertex AI AutoSxS Pipeline that generates the judgments and a set of AutoSxS metrics using the generated judgments.
• Print the judgments and AutoSxS metrics.
• Clean up the resources created in this notebook.

Tutorial steps

• Fetch the dataset from the public source.
• Preprocess the data locally and save test data in Cloud Storage.
• Create and run a Vertex AI AutoSxS Pipeline that generates the judgments and evaluates the two candidate models using the generated judgments.
• Print the judgments and evaluation metrics.
• Clean up the resources created in this notebook.

Tutorial steps

• Upload a pretrained model as a Vertex AI model resource.
• Generate batch prediction requests.
• Interpret the statistics, visualizations, other data reported by the model monitoring feature.

Tutorial steps

• Train an AutoML model.
• Deploy the model resource to a Vertex AI endpoint resource.
• Configure the endpoint resource for model monitoring.
• Generate synthetic prediction requests for skew.
• Generate synthetic prediction requests for drift.
• Wait for email alert notification.

Tutorial steps

• 1. Train an AutoML image classification model.
• 2. Create an endpoint.
• 3. Deploy the model to the endpoint, and configure for model monitoring.
• 4. Submit a online prediction containing both in and out of distribution images.
• 5. Use Model Monitoring to calculate anomaly score on each image.
• 6. Identify the images in the online prediction request that are out of distribution.

Tutorial steps

• Download a pretrained custom tabular model.
• Upload the pretrained model to Vertex AI Model Registry.
• Deploy the model resource to a Vertex AI endpoint resource.
• Configure the endpoint resource for model monitoring.
• Generate synthetic prediction requests to simulate skewness.
• Wait for email alert notifications.
• Generate synthetic prediction requests to simulate drift.
• Wait for email alert notifications.

Tutorial steps

• Download a pretrained custom tabular model.
• Upload the pretrained 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.

Tutorial steps

• Download a pretrained custom tabular model.
• Upload the pretrained 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.

Tutorial steps

• Download a pretrained XGBoost model.
• Upload the pretrained model to Vertex AI Model Registry.
• Deploy the model resource to a Vertex AI endpoint resource.
• Configure the endpoint resource for model monitoring

Tutorial steps

• Upload a pretrained 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.
• Initialize the baseline distribution for model monitoring.
• Generate synthetic prediction requests.
• Understand how to interpret the statistics, visualizations, other data reported by the model monitoring feature.

Tutorial steps

Tutorial steps

Tutorial steps

• Create and register a first version of a model to Vertex AI Model Registry.
• Create and register a second version of a model to Vertex AI Model Registry.
• Updating the model version which is the default.
• Deleting a model version.
• Retraining the next model version.

Tutorial steps

• Create a KFP pipeline that creates a Vertex AI Dataset.
• Add a component to the pipeline that trains an AutoML tabular classification model resource.
• Add a component that creates a Vertex AI endpoint resource.
• Add a component that deploys the model resource to the endpoint resource.
• Compile the KFP pipeline.
• Execute the KFP pipeline using Vertex AI Pipelines.

Tutorial steps

• Import a pretrained (blessed) model to the Vertex AI Model Registry.
• Import synthetic model evaluation metrics to the corresponding (blessed) model.
• Create a Vertex AI endpoint resource
• Deploy the blessed model to the endpoint resource.
• Create a Vertex AI Pipeline that runs the following steps

Tutorial steps

• Create a KFP pipeline using control flow components
• Compile the KFP pipeline
• Execute the KFP pipeline using Vertex AI Pipelines

Tutorial steps

• Create a KFP pipeline

Tutorial steps

• Create a dataset in BigQuery.
• Set some data aside from the source dataset for batch prediction.
• Create a custom python package for training application.
• Upload the python package to Cloud Storage.
• Create a Vertex AI Pipeline that

Tutorial steps

• Construct a pipeline for

Tutorial steps

• Create a custom component with a selfcontained training job.
• Execute pipeline using componentlevel settings for machine resources
• Convert the selfcontained training component into a Vertex AI CustomJob.
• Execute pipeline using customjoblevel settings for machine resources

Tutorial steps

• Create a KFP pipeline

Tutorial steps

• Create a KFP pipeline that creates a Dataset resource.
• Add a component to the pipeline that trains an AutoML tabular regression Model resource.
• Add a component that creates an Endpoint resource.
• Add a component that deploys the Model resource to the Endpoint resource.
• Compile the KFP pipeline.
• Execute the KFP pipeline using Vertex AI Pipelines.

Tutorial steps

• Create a KFP pipeline

Tutorial steps

• Creating a component for Dataflow job that ingests data to BigQuery.
• Creating a component for preprocessing steps to run on the data in BigQuery.
• Creating a component for training a logistic regression model using BigQuery ML.
• Building and configuring a Kubeflow DSL pipeline with all the created components.
• Compiling and running the pipeline in Vertex AI Pipelines.

Tutorial steps

• Create a KFP pipeline

Tutorial steps

• Create a KFP pipeline

Tutorial steps

• Build Python functionbased 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

Tutorial steps

• Create KFP components

Tutorial steps

• Import a pretrained (champion) model to the Vertex AI Model Registry.
• Import synthetic model training evaluation metrics to the corresponding (champion) model.
• Create a Vertex AI endpoint resource.
• Deploy the champion model to the endpoint resource.
• Import additional (contender) versions of the deployed model.
• Import synthetic model training evaluation metrics to the corresponding (contender) models.
• Create a Vertex AI Pipeline that runs the following steps

Tutorial steps

• Define and compile a Vertex AI pipeline.
• Specify which service account to use for a pipeline run.
• Run the pipeline using Vertex AI SDK for Python and REST API.

Tutorial steps

• 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.

Tutorial steps

• Create a Vertex AI custom TrainingPipeline for training a model.
• Train a TensorFlow model.
• Send batch prediction job.

Tutorial steps

• Download the model artifacts from TensorFlow Hub.
• Create Triton serving configuration file for the model.
• Construct a custom container, with Triton serving image, for model deployment.
• Upload the model as a Vertex AI model resource.
• Deploy the Vertex AI model resource to a Vertex AI endpoint resource.
• Make a prediction request.
• Undeploy the model resource and delete the endpoint.

Tutorial steps

• Download pretrained tabular classification model artifacts for a TensorFlow 1.x estimator.
• Upload the TensorFlow estimator model as a Vertex AI Model resource.
• Create an Endpoint resource.
• Deploy the Model resource to an Endpoint resource.
• Make an online raw prediction to the Model resource instance deployed to the Endpoint resource.

Tutorial steps

Tutorial steps

• Creating a Private Endpoint resource.
• Configure a VPC peering connection.
• Configuring the serving binary of a Model resource for deployment to a Private Endpoint resource.
• Deploying a Model resource to a Private Endpoint resource.
• Send a prediction request to a Private Endpoint

Tutorial steps

• Load a pretrained text generation model.
• Make a nonstreaming 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.

Tutorial steps

• Download a pretrained image model from PyTorch
• Create a custom model handler
• Package model artifacts in a model archive file
• Upload model for deployment
• Deploy model for prediction
• Make online predictions
• Make batch predictions

Tutorial steps

• Package training application into a Python source distribution
• Configure and run training job in a prebuilt container
• Package model artifacts in a model archive file
• Upload model for deployment
• Deploy model using a prebuilt container for prediction
• Make online predictions

Tutorial steps

• Prepare the training script
• Submit a Ray job using the Ray Jobs API
• Download a trained image model from PyTorch
• Create a custom model handler
• Package model artifacts in a model archive file
• Register model in Vertex AI Model Registry
• Deploy model in Vertex AI Endpoint
• Make online predictions

Tutorial steps

• Create a cluster.
• List existing clusters.
• Get a cluster.
• Manually scale up the cluster, then scale down the cluster.
• Autoscaling a cluster.
• Delete existing clusters.

Tutorial steps

• Create custom Ray on Vertex AI container image
• Create a Ray cluster on Vertex AI using custom container image
• Run Spark interactively on the cluster using RayDP
• Run Spark application on cluster via Ray Job API
• Read files from Google Cloud Storage in Spark application
• Pandas UDF in Spark application on Ray on Vertex AI
• Delete the Ray cluster on Vertex AI

Tutorial steps

• Create a PyTorch distributed training application
• Package the training application with prebuilt containers
• Create a custom job on Vertex AI with Reduction Server
• Submit and monitor the job

Tutorial steps

• Set your task name, and Cloud Storage prefix
• Copy AutoML video demo train data for creating managed dataset
• Create a dataset on Vertex AI.
• Configure a training job
• Launch a training job and create a model on Vertex AI
• Copy AutoML Video Demo Prediction Data for creating batch prediction job
• Perform batch prediction job on the model

Tutorial steps

• Create utility functions to download data and prepare csv files for creating Vertex AI managed dataset
• Download Data
• Prepare CSV Files for creating managed dataset
• Create custom training Python package
• Create TensorFlow Serving container
• Run custom Python package training with managed text dataset
• Deploy a model and create an endpoint on Vertex AI
• Predict on the endpoint
• Create a Batch Prediction job on the model

Tutorial steps

• Setup the project.
• Download the prediction data of pretrain model on Syn2 data.
• Visualize and understand the feature importance based on the masks output.
• Clean up the resource created by this tutorial.

Tutorial steps

• Train the BigQuery ML ARIMA_PLUS model.
• View BigQuery ML model evaluation.
• Make a batch prediction with the BigQuery ML model.

Tutorial steps

• 1. Train the Prophet models.
• 1. View the evaluation metrics.
• 1. Make a batch prediction with the Prophet models.

Tutorial steps

• Create a TabNet CustomJob. This is the best option if you know which hyperparameters to use for training.
• Create a TabNet HyperparameterTuningJob. This allows you to get the best set of hyperparameters for your dataset.

Tutorial steps

• Create a Wide & Deep CustomJob. This is the best option if you know which hyperparameters to use for training.
• Create a Wide & Deep HyperparameterTuningJob. This allows you to get the best set of hyperparameters for your dataset.

Tutorial steps

• Create docker repository & config.
• Create a custom container image with your customized training code.
• Setup service account and Google Cloud Storage buckets.
• Create & launch your custom training job with your custom container.

Tutorial steps

• Setup service account and Cloud Storage buckets.
• Write your customized training code.
• Package and upload your training code to Cloud Storage.
• Create & launch your custom training job with Vertex AI TensorBoard enabled for near real time monitoring.

Tutorial steps

• Adapt TensorFlow runs to log hyperparameters and metrics.
• Start runs and log them all under one parent directory.
• Visualize the results in Vertex AI TensorBoard's HParams dashboard.

Tutorial steps

• Setup a service account and a Cloud Storage bucket
• Create a Vertex AI TensorBoard instance
• Create and run a custom training job that enables Profiler
• View the Profiler dashboard to debug your model training performance

Tutorial steps

• Prepare your custom training code and load your training code as a Python package to a prebuilt container
• Create and run a custom training job that enables Profiler
• View the Profiler dashboard to debug your model training performance

Tutorial steps

• Setup a service account and Google Cloud Storage buckets.
• Construct a KFP pipeline with your custom training code.
• Compile and execute the KFP pipeline in Vertex AI Pipelines with Vertex AI TensorBoard enabled for near real time monitoring.

Tutorial steps

• Training using a Python package.
• Report accuracy when hyperparameter tuning.
• Save the model artifacts to Cloud Storage using Cloud StorageFuse.

Tutorial steps

• Training using a Python package.
• Save the model artifacts to Cloud Storage using Cloud StorageFuse.
• Construct a FastAPI prediction server.
• Construct a Dockerfile deployment image for the server.
• Test the deployment image locally (optional and not for Colab users).
• Create a Vertex AI model resource.
• Run a batch prediction job.
• Deploy the model to an endpoint and send online prediction requests.
• Clean up the created resources.

Tutorial steps

Tutorial steps

• Modify training application code for automated hyperparameter tuning.
• Containerize training application code.
• Configure and launch a hyperparameter tuning job with the Vertex AI Python SDK.

Tutorial steps

• Train using a Python training application package.
• Report accuracy during hyperparameter tuning.
• Save the model artifacts to Cloud Storage using Cloud StorageFuse.
• List the best model.

Tutorial steps

• Setting up your Google Cloud project

Tutorial steps

• Building a custom container using Artifact Registry and Docker.
• Creating a Vertex AI tensorboard instance to store your Vertex AI experiment.
• Run a Vertex AI training job using Vertex AI SDK for Python.

Tutorial steps

• Create training package for the text classification model.
• Train the model with custom training on Vertex AI.
• Check the created model artifacts.
• Create a custom container for predictions.
• Deploy the trained model to a Vertex AI Endpoint using the custom container for predictions.
• Send online prediction requests to the deployed model and validate.
• Clean up the resources created in this notebook.

Tutorial steps

• Writing a custom training script that creates your train & test datasets and trains the model.
• Running a CustomTrainingJob using Vertex AI SDK for Python.

Tutorial steps

• Create a shell script to start an ETCD cluster on the master node
• Create a training script using code from PyTorch Elastic's GitHub repository
• Create containers that download the data, and start an ETCD cluster on the host
• Train the model using multiple nodes with GPUs

Tutorial steps

• Configure the PROJECT_ID and LOCATION variables for your Google Cloud project.
• Create a Cloud Storage bucket to store your model artifacts.
• Build a custom Docker container that hosts your training code and push the container image to Artifact Registry.
• Run a Vertex AI SDK CustomContainerTrainingJob

Tutorial steps

• 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.

Tutorial steps

• Create ANN index.
• Create an index endpoint with VPC Network.
• Deploy ANN index.
• Perform online query.

Tutorial steps

• 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.

Tutorial steps

• Create ANN Index and Brute Force Index.
• Create an IndexEndpoint with VPC Network.
• Deploy ANN Index and Brute Force Index.
• Perform online query.
• Compute recall.

Tutorial steps

Tutorial steps

• Hyperparameter tuning with Random algorithm.
• Hyperparameter tuning with Vertex AI Vizier (Bayesian) algorithm.
• Suggesting trials and updating results for Vertex AI Vizier study

Tutorial steps

• Fetch the required data from BigQuery
• Preprocess the data
• Train a TensorFlow (>=2.4) classification model
• Evaluate the loss for the trained model
• Automate the notebook execution using the executor feature
• Save the model to a Cloud Storage path
• Clean up the created resources

Tutorial steps

• Loading the dataset using "BigQuery in Notebooks".
• Performing exploratory data analysis on the dataset.
• Feature selection and preprocessing.
• Building a linear regression model using scikitlearn.
• Configuring the model for Vertex Explainable AI.
• Deploying the model to Vertex AI.
• Testing the deployed model.
• Clean up.

Tutorial steps

• Explore data
• Model with BigQuery and the ARIMA model
• Evaluate the model
• Evaluate the model results using BigQuery ML (on training data)
• Evaluate the model results MAE, MAPE, MSE, RMSE (on test data)
• Use the executor feature

Tutorial steps

• Using Python & SQL to query public data in BigQuery
• Exploring the dataset using BigQuery INFORMATION_SCHEMA
• Creating interactive elements to help explore interesting parts of the data
• Doing some exploratory correlation and time series analysis
• Creating static and interactive outputs (data tables and plots) in the notebook
• Saving some outputs to Cloud Storage

Tutorial steps

• Installation of required libraries
• Reading the dataset from a Cloud Storage bucket
• Performing exploratory analysis on the dataset
• Preprocessing the dataset
• Training a random forest model using scikitlearn
• Saving the model to a Cloud Storage bucket
• Creating a Vertex AI model resource and deploying to an endpoint
• Running the WhatIf Tool on test data
• Undeploying the model and cleaning up the model resources

Tutorial steps

• Explore the data exported from Google Analytics 4 in BigQuery.
• Prepare the training data using demographic, behavioral data, and labels (churn/notchurn).
• Train an XGBoost model using BigQuery ML.
• Evaluate the model using BigQuery ML.
• Use BigQuery ML to predict which users are likely to churn.

Tutorial steps

• Loading the required dataset from a Cloud Storage bucket.
• Analyzing the fields present in the dataset.
• Selecting the required data for the predictive maintenance model.
• Training an XGBoost regression model for predicting the remaining useful life.
• Evaluating the model.
• Running the notebook endtoend as a training job using Executor.
• Deploying the model on Vertex AI.
• Clean up.

Tutorial steps

• Load the required dataset from a Cloud Storage bucket.
• Analyze the fields present in the dataset.
• Process the data to build a model.
• Build a BigQuery ML forecast model on the processed data.
• Get forecasted values from the BigQuery ML model.
• Interpret the forecasts to identify the best prices.
• Clean up.

Tutorial steps

• Loading the required data.
• Preprocessing the data.
• Selecting the required data for the model.
• Loading the dataset into Vertex AI managed datasets.
• Training a sentiment model using AutoML Text training.
• Evaluating the model.
• Deploying the model on Vertex AI.
• Getting predictions.
• Clean up.

Tutorial steps

• Setting up a Google Cloud project and Dataproc cluster.
• Configuring the sparkbigqueryconnector.
• Ingesting data from BigQuery into a Spark DataFrame.
• Preprocessing ingested data.
• Querying the most frequently used programming language in monoglot repos.
• Querying the average size (MB) of code in each language stored in monoglot repos.
• Querying the languages files most frequently found together in polyglot repos.
• Writing the query results back into BigQuery.
• Deleting the resources created for this notebook tutorial.

Tutorial steps

• Sets up a Google Cloud project and Dataproc cluster.
• Creates a Cloud Storage bucket and a BigQuery dataset.
• Configures the sparkbigqueryconnector.
• Ingests BigQuery data into a Spark DataFrame.
• Performa Exploratory Data Analysis (EDA).
• Visualizes the data with samples.
• Cleans the data.
• Selects features.
• Trains the model.
• Outputs results.
• Saves the model to a Cloud Storage bucket.
• Deletes the resources created for the tutorial.