> For the complete documentation index, see [llms.txt](https://docs.datasaur.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datasaur.ai/assisted-labeling/ml-assisted-labeling/google-vertex-ai.md).
# Google Vertex AI
Enable your integration with models from Google Vertex AI.
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**Supported labeling types**: Row labeling.
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**ML-assisted labeling** supports models from Vertex AI. If you already have data and training pipelines in Vertex AI, you can utilize your trained model to enhance the performance.
### Enable ML-assisted labeling
To use the models from Vertex AI, select **Google Vertex AI** as the service provider.
ML-assisted labeling with Google Vertex AI
Once selected, you will see the following fields:
* **Target text**: Select the input text used as reference.
* **Target question**: Select the output column to predict.
* **API URL**: The endpoint from Google Vertex AI. The format will follow the sample below:
`https://-aiplatform.googleapis.com/ui/projects//locations//endpoints/:predict`
* **Faster prediction speed**: Enables faster predictions by processing requests on the backend.
### Setup your model
You can view the endpoint URL by clicking its name after successful deployment.
Endpoint page from Google Vertex AI
To allow **ML-assisted labeling** to run your model, you need to grant the Datasaur Service Account access as Vertex AI Administrator. Follow the steps below:
1. Navigate to the project where your model is located.
2. Go to **IAM & Admin**.
3. Click on the **IAM** in the left sidebar.
IAM page menu
4. Click **Grant access**.
5. In the **New principals** field, fill in the **Service account email**:
6. In the role field, select **Vertex AI Administrator**.
7. Click **Save** to complete the setup.
### Start prediction
After configuring the settings above, copy the endpoint URL and paste it into the **API URL** field. Click **Predict labels** to generate and retrieve predictions from your model.
