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.
Select or create a 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
gcloud config set project PROJECT_ID && projectid=PROJECT_ID && echo $projectidReplace 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:
gcloud projects add-iam-policy-binding PROJECT_ID --member="user:EMAIL_ADDRESS" --role=ROLE
PROJECT_IDwith your project ID.
EMAIL_ADDRESSwith your email address.
ROLEwith each individual role.
The Vertex AI User (
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.
Follow the next page of this tutorial to create a tabular dataset and train a classification model.