Hello image data: Clean up your project

Clean up the Google Cloud resources that you created to train your image classification model and get predictions from it. Follow these steps to avoid incurring unexpected charges from some of the resources.

This tutorial has several pages:

  1. Setting up your project and environment.

  2. Creating an image classification dataset and importing images.

  3. Training an AutoML image classification model.

  4. Deploying a 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, in the Vertex AI section, go to the Endpoints page.

    Go to the Endpoints page

  2. Find your endpoint, hello_automl_image. On that row, click View more . Then click Remove endpoint.

  3. In the Remove endpoint dialog, click Confirm.

Delete your model

  1. In the Google Cloud Console, in the Vertex AI section, go to the Models page.

    Go to the Models page

  2. Find your model. On that row, click View more . Then click 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. On that row, click View more . Then click Delete dataset.

Clean up your Cloud Shell session

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

Delete your Cloud Storage bucket

In your Cloud Shell session, run the following command:

gsutil -m rm -rf gs://BUCKET_NAME

Replace BUCKET_NAME with the name of the Cloud Storage bucket that you created when reading the first page of this tutorial.

What's next