Get started with MedGemma

  • MedGemma can be run locally for experimentation with a provided notebook.

  • For production-grade, online applications, MedGemma can be deployed on Vertex AI as a scalable HTTPS endpoint through Model Garden.

  • You can fine-tune MedGemma with your own medical data to improve its performance for specific use cases using a provided notebook.

  • For processing larger datasets in a batch workflow, you can launch a Vertex AI batch prediction job.

  • You can contact the project team through GitHub Discussions, GitHub Issues, or email for support and feedback.

You can get started in 4 ways:

Run it locally

Download the MedGemma model you are interested in from Hugging Face and run it locally.

This is the recommended option if you want to experiment with MedGemma and don't need to handle a high volume of data. Note to run the 27B model without quantization you will need to use Colab Enterprise. Our GitHub repository includes a notebook that you can use to explore the model.

Deploy your own online service

MedGemma can be deployed as a highly available and scalable HTTPS endpoint on Vertex AI. The easiest way is through Model Garden.

This option is ideal for production-grade, online applications with low latency, high scalability and availability requirements. Refer to Vertex AI's service level agreement (SLA) and pricing model for online predictions.

A sample notebook is available to help you get started quickly.

Fine-tune MedGemma

MedGemma can be fine-tuned using your own medical data to optimize its performance for your use cases. A sample notebook is available that you can adapt for your data and use case.

Launch a batch job

For larger datasets in a batch workflow, it's best to launch it as a Vertex AI batch prediction job. Note that Vertex AI's SLA and pricing model are different for batch prediction jobs.

Contact

You can reach out in several ways: