Hello text data: Clean up your project

Starting on September 15, 2024, you can only customize classification, entity extraction, and sentiment analysis objectives by moving to Vertex AI Gemini prompts and tuning. Training or updating models for Vertex AI AutoML for Text classification, entity extraction, and sentiment analysis objectives will no longer be available. You can continue using existing Vertex AI AutoML Text models until June 15, 2025. For a comparison of AutoML text and Gemini, see Gemini for AutoML text users. For more information about how Gemini offers enhanced user experience through improved prompting capabilities, see Introduction to tuning. To get started with tuning, see Model tuning for Gemini text models

To learn more, run the "Create, train, and deploy an AutoML text classification model" Jupyter notebook in one of the following environments:

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench user-managed notebooks | View on GitHub

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

• To learn about additional ways to train ML models on Vertex AI, try one of the other Vertex AI tutorials.

• Read an overview of how Vertex AI works.