model_garden/notebooks/official/automl/README.md

AutoML Tabular Training and Prediction

Learn how to train and make predictions on an AutoML model based on a tabular dataset.

The steps performed include the following:

• 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.

Create, train, and deploy an AutoML text classification model

Learn how to use AutoML to train a text classification model.

The steps performed include:

• Create a Vertex AI Dataset.
• Train an AutoML text classification Model resource.
• Obtain the evaluation metrics for the Model resource.
• Create an Endpoint resource.
• Deploy the Model resource to the Endpoint resource.
• Make an online prediction
• Make a batch prediction

AutoML training video classification model for batch prediction

Learn how to create an AutoML video classification model from a Python script, and then do a batch prediction using the Vertex AI SDK.

The steps performed include:

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

• Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.

• Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

AutoML training text entity extraction model for online prediction

Learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK.

The steps performed include:

• 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.

AutoML tabular forecasting model for batch prediction

Learn how to create an AutoML tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK.

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.

AutoML training image object detection model for batch prediction

Learn how to create an AutoML image object detection model from a Python script, and then do a batch prediction using the Vertex AI SDK.

The steps performed include:

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

• Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.

• Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

AutoML training video action recognition model for batch prediction

Learn how to create an AutoML video action recognition model from a Python script, and then do a batch prediction using the Vertex AI SDK.

The steps performed include:

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

• Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.

• Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

AutoML Tabular Pipeline

Learn how to create two regression models using [Vertex Pipelines](https://cloud.

The steps performed are:

• 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.

AutoML training text sentiment analysis model for online prediction

Learn how to create an AutoML text sentiment analysis model and deploy for online prediction from a Python script using the Vertex SDK.

The steps performed include:

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

Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS

Learn how to create an BQML ARIMA_PLUS model using a training [Vertex AI Pipeline](https://cloud.

The steps performed are:

• Train the BQML ARIMA_PLUS model.
• View BQML model evaluation.
• Make a batch prediction with the BQML model.
• Create a Vertex AI Dataset resource.
• Train the Vertex AI Forecasting model.
• View the Model evaluation.
• Make a batch prediction with the Model.

AutoML training tabular regression model for online prediction using BigQuery

Learn how to create an AutoML tabular regression model and deploy for online prediction from a Python script using the Vertex AI SDK.

The steps performed include:

• 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.

AutoML training video object tracking model for batch prediction

Learn how to create an AutoML video object tracking model from a Python script, and then do a batch prediction using the Vertex SDK.

The steps performed include:

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

• Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.

• Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

AutoML training tabular regression model for batch prediction using BigQuery

Learn how to create an AutoML tabular regression model and deploy it for batch prediction using the Vertex AI SDK for Python.

The steps performed include:

• 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.