Utilizing the Omneer SDK, developers can deliver:

• Personalized AI and machine learning tools

• Advanced disease progression diagnosis and tracking

• Creation of digital twins for individualized healthcare

• Instant no-code interfaces for rapid deployment

• High-performing, reliable cloud infrastructure

• Flexibility to define resources (CPU, GPU, etc.) for serverless execution

• Omneer SDK continues to be a ground-breaking platform for personalized healthcare solutions. Browse our collection of existing and actively maintained solutions at Omneer Community.

See the SDK in action by following the steps below to register your first workflow with Omneer.

First, install omneer through pip.

$ pip install omneer

Then, create some boilerplate code for your new workflow.

$ omneer init diagnosis

The registration process, which could take a few minutes depending on your network connection, involves building a Docker image with your workflow code, serializing the code, registering it with your Omneer account, and pushing the Docker image to a managed container registry.

Upon successful registration, your new workflow should be visible in your Omneer Console.

For issues with registration or other queries, please raise an issue on GitHub.

Omneer SDK is distributed via pip. We recommend installing it in a clean virtual environment for the best user experience.

Virtualenv is our recommended tool for creating isolated Python environments.

Virtualenvwrapper is recommended.

pip install omneer

Omneer Examples features list of well-curated workflows developed by the Omneer team.

• Parkinson's Diagnosis
• Parkinson's Progression
• Parkinson's Personalized

We'll maintain a growing list of well documented examples developed by our community members here. Please open a pull request to feature your own:

Parkinson's Diagnosis

• Early Detection