edge-cloud collaboration

**Edge-Cloud Collaboration** is the **architectural pattern where edge and cloud systems work together for ML inference and training** — splitting the workload between lightweight edge models (fast, private, local) and powerful cloud models (accurate, resource-rich, global) for optimal performance. **Collaboration Patterns** - **Edge Inference, Cloud Training**: Train in the cloud, deploy to edge — the simplest pattern. - **Cascade**: Edge model handles easy cases, cloud model handles hard cases — reduces cloud cost. - **Split Inference**: Run part of the model on edge, send intermediate features to cloud for completion. - **Edge Training**: Train locally on edge, periodically synchronize with cloud — federated pattern. **Why It Matters** - **Best of Both**: Edge provides low latency and privacy; cloud provides accuracy and compute power. - **Cost Optimization**: Only send hard cases to the cloud — 90%+ of inference stays on edge. - **Semiconductor**: Edge models in the fab for real-time decisions, cloud models for offline analytics and model updates. **Edge-Cloud Collaboration** is **distributed intelligence** — combining edge speed and privacy with cloud power and scale for optimal ML system design.