# Google Gemini integration
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/gemini-google.html

> Leverage LLMs proposed by hyperscalers via the Custom Model Workshop.

# Google Gemini integration

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/gemini-app)

DataRobot allows you to leverage LLMs proposed by hyperscalers via the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html#custom-model-workshop).

This AI Accelerator demonstrates how to implement a Streamlit application based on the Google Gemini LLM and host it on the DataRobot platform. The user of this AI Accelerator is expected to be familiar with the custom model deployment process and custom metrics creation in DataRobot as well as with Google Vertex AI.

This accelerator requires the service account for the Vertex AI project. The following steps outline the accelerator workflow.

1. Createcredentialswith a GCP service account (base64 encoded).
2. Optional. Deploy a guard model from theDataRobot global models.
3. Deploya text model (Gemini Pro).
4. Deploya multimodal model (Gemini Pro Vision).
5. Create custom metricsfor both deployments (text and multimodal).
6. Deploy a Streamlit appto DataRobot.

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