Moving inference from cloud infrastructure to an edge device changes more than the deployment target. Compute, memory, power, connectivity, thermal limits, and hardware variation become part of the software architecture.
Model quality is only one constraint
An accurate model may still be unusable if it exceeds the device's latency, memory, or power envelope.
Operate through imperfect connectivity
Edge systems should define local inference, buffering, retry behavior, synchronization, and safe degradation explicitly.
Updates and telemetry matter
Teams need controlled model and application updates, device versioning, health monitoring, and drift or field-failure detection. Reliable edge AI is a systems problem.