NeuraFabric’s Distributed Neuromorphic Architecture©(NeuraFabric DNA©) delivers real-time, self-optimizing intelligent systems that autonomously adapt and learn in dynamic environments. Unlike traditional machine learning models that require constant retraining, NeuraFabric DNA© provides scalablility across distributed networks, reducing your operational costs and complexity, while enhancing decision-making capabilities.

• Improved accuracy: Incoporating new unseen private data allows your model to become more accurate.

• Incremental learning: The system learns continuously by integrating new private data without redoing the full training process.

• Efficient updates: Instead of retraining the model from the beginning, it modifies specific parts or parameters based on new insights.

• Applications: It's useful in environments where data evolves over time, such as recommendation systems, dynamic pricing, or real-time analytics, where frequent retraining is impractical or unnecessary. Also, some data must remain private either for regulatory reasons or competitive reasons, requiring a distributed appraoch to model learning.

• Cost reductions: Only pay for your private data to be incorporated. This distributes the cost across all collaborative efforts, allowing for federated and distributed cost models. You also don't have to pay data egress costs as there is no centralizing of data required.