Transforming Rare Disease Research with Federated Data Models

Sponsored by UBC & SymetryML

The rare disease registry landscape consists of small, disjointed data sets stored across different national registries and localized consortia. These data silos, coupled with strict data privacy regulations, limit the collaboration that is needed by researchers to enhance analysis.

In today’s webinar, we’ll show you how UBC has partnered with SymetryML and their unique Federated Learning 2.0 platform to overcome data silos and quickly & easily build machine learning models to improve research and outcomes.

Learning Objectives:
• Improving data sharing & collaboration in registries
• What is federated learning?
• How is SymetryML’s federated learning 2.0 different/better?
• Efficiently building and scaling machine learning models for enhanced analysis

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