Machine Learning at the Edge: Model Deployment & Training

Machine learning at the edge combines model optimization (quantization, pruning) with on-device inference frameworks to run sophisticated ML models on resource-constrained hardware. Federated learning allows models to improve from distributed edge data without centralizing sensitive information.