Editing Machine Learning at the Edge (section)

==='''Synthesis'''===

In summary, edge-oriented machine learning optimization requires an integrated approach that combines model-level compression with system-level orchestration. Techniques such as quantization, structured and unstructured pruning, and knowledge distillation reduce the computational footprint and memory requirements of deep learning models, enabling deployment on resource-constrained devices without substantial loss in inference accuracy. Concurrently, dynamic workload partitioning, heterogeneity-aware scheduling, and adaptive runtime profiling allow the system to allocate tasks across edge and cloud tiers based on real-time availability of compute, bandwidth, and energy resources. This joint optimization across model architecture and execution environment is essential to meet the latency, privacy, and resilience demands of edge AI deployments.