Expand dataset by orders of magnitude by querying data across similar locations for various conditions and long-tail cases.
Enhance the robustness of your model by curating training and evaluation data to best suit your local test distribution.
Measure and track the reliability of your model at each location and determine operational domains adaptively.
Identify anomalies or adverse conditions in real-time and determine safe routing or behaviors. React in real-time with local model updates.
Reliable real-world neural nets at scale.
Optimize your model for on-device inference on the edge using state of the art neural compression. Leverage federated learning to perform distributed on-device training.
Scaling deep learning to multiple locations in the real-world safely & reliably is challenging.
NuronLabs is here to help.