Hello text data: Clean up your project

Clean up the Google Cloud resources that you created during this tutorial. Follow these steps to avoid incurring unexpected charges.

This tutorial has several pages:

  1. Setting up your project and environment.

  2. Creating a text classification dataset .

  3. Training an AutoML text classification model.

  4. Deploy model to an endpoint and send a prediction.

  5. Cleaning up your project.

Each page assumes that you have already performed the instructions from the previous pages of the tutorial.

Delete Vertex AI resources

This section describes how to delete the following project resources: endpoint, model, dataset, and Cloud Storage bucket.

Delete your endpoint

  1. In the Google Cloud console, go to the Endpoints page.

    Go to Endpoints

  2. Click your endpoint.

  3. On the endpoint details page, find the row for your model. Click  View more > Undeploy model from endpoint.

  4. In the Undeploy model from endpoint dialog, click Undeploy.

  5. Go back to the Endpoints page.

  6. Find your endpoint, click  View more > Delete endpoint.

  7. In the Delete endpoint dialog, click Confirm.

Delete your model

  1. In the Google Cloud console, go to the Model Registry page.

    Go to the Model Registry page

  2. Find your model, click  View more >  Delete model.

  3. In the Delete model dialog, click Delete.

Delete your dataset

  1. In the Google Cloud console, in the Vertex AI section, go to the Datasets page.

    Go to the Datasets page

  2. Find your dataset, click  View more >  Delete dataset.

Delete your Cloud Storage bucket

  1. In the Google Cloud console, go to the Cloud Storage Buckets page.

    Go to Buckets

  2. Click the checkbox for the bucket that you want to delete.
  3. To delete the bucket, click Delete, and then follow the instructions.

Cloud Shell session

No action is required. Cloud Shell incurs no charges, and it automatically deletes your home disk after a period of inactivity.

What's next