Try Gemini 1.5 Pro, our most advanced multimodal model in Vertex AI, and see what you can build with a 1M token context window. Try Gemini 1.5 Pro, our most advanced multimodal model in Vertex AI, and see what you can build with a 1M token context window.

Hello tabular data: Set up your project and environment

This tutorial walks you through the required steps to train and get predictions from your tabular data model in the Google Cloud console. 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.

For this part of the tutorial, you set up your Google Cloud project to use Vertex AI and a Cloud Storage bucket that contains the documents for training your AutoML model.

Set up your project and environment

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

Go to project selector
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.
Make sure that billing is enabled for your Google Cloud project.
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

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.

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

gcloud services enable iam.googleapis.com  compute.googleapis.com notebooks.googleapis.com storage.googleapis.com aiplatform.googleapis.com

Grant roles to your Google Account. Run the following command once for each of the following IAM roles: roles/aiplatform.user, roles/storage.admin
```
gcloud projects add-iam-policy-binding PROJECT_ID --member="user:EMAIL_ADDRESS" --role=ROLE
```
- Replace PROJECT_ID with your project ID.
- Replace EMAIL_ADDRESS with your email address.
- Replace ROLE with each individual role.

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 lets you store the document's training dataset in Cloud Storage.

What's next

Follow the next page of this tutorial to create a tabular dataset and train a classification model.

Overview

Create a dataset and train an AutoML classification model