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Showing content from https://cloud.google.com/vertex-ai/docs/tutorials/image-classification-automl below:

Hello image data: Set up your project and environment | Vertex AI

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If you plan to use the Vertex AI SDK for Python, make sure that the service account initializing the client has the Vertex AI Service Agent (roles/aiplatform.serviceAgent) IAM role.

You'll set up your Google Cloud project to use Vertex AI. Then create a Cloud Storage bucket and copy image files to use for training an AutoML image classification model.

This tutorial has several pages:

1. Set up your project and environment.

2. Create an image classification dataset, and import images.

3. Train an AutoML image classification model.

4. Evaluate and analyze model performance.

5. Deploy a model to an endpoint, and send a prediction.

6. Clean up your project.

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

Complete the following steps before using Vertex AI functionality.

1. In the Google Cloud console, go to the project selector page.

   Go to project selector

2. Select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

3. Verify that billing is enabled for your Google Cloud project.

4. Open Cloud Shell. Cloud Shell is an interactive shell environment for Google Cloud that lets you manage your projects and resources from your web browser.
Go to Cloud Shell
5. In the Cloud Shell, set the current project to your Google Cloud project ID and store it in the projectid shell variable:

     gcloud config set project PROJECT_ID &&
     projectid=PROJECT_ID &&
     echo $projectid

   Replace PROJECT_ID with your project ID. You can locate your project ID in the Google Cloud console. For more information, see Find your project ID.

6. Enable the IAM, Compute Engine, Notebooks, Cloud Storage, and Vertex AI APIs.

   Enable the APIs

7. Make sure that you have the following role or roles on the project: roles/aiplatform.user, roles/storage.admin

   Check for the roles

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

      Go to IAM
   2. Select the project.

   3. In the Principal column, find all rows that identify you or a group that you're included in. To learn which groups you're included in, contact your administrator.

   4. For all rows that specify or include you, check the Role column to see whether the list of roles includes the required roles.
   Grant the roles

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

      Go to IAM
   2. Select the project.
   3. Click person_add Grant access.

   4. In the New principals field, enter your user identifier. This is typically the email address for a Google Account.

   5. In the Select a role list, select a role.
   6. To grant additional roles, click add Add another role and add each additional role.
   7. Click Save.
The Vertex AI User (roles/aiplatform.user) IAM role provides access to use all resources in Vertex AI. The Storage Admin (roles/storage.admin) role you store the document's training dataset in Cloud Storage.

Follow the next page of this tutorial to use the Google Cloud console to create an image classification dataset and import images hosted in a public Cloud Storage bucket.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

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