May 1, 2024

Edge Computing

Edge AI for IoT Systems

Running inference on-device cuts latency, reduces bandwidth costs, and keeps data closer to the source.

Key highlights

  • On-device inference for real-time decisions
  • Low-latency alerts for critical events
  • Model compression to fit constrained hardware

Deployment approach

We design models with hardware constraints in mind, then pair them with lightweight update pipelines so fleets stay current without downtime.

What to measure

Track inference latency, drift in sensor data, and update success rates to keep edge systems stable at scale.