Edge AI on a $35 Raspberry Pi

Everyone assumes AI inference needs beefy cloud GPUs. We proved them wrong — running production computer vision on $35 Raspberry Pi boards, processing live camera feeds with zero cloud dependency.

Why Edge, Not Cloud?

[Expand: Bandwidth costs of streaming video to cloud. Latency requirements for real-time detection. Privacy constraints. Unreliable connectivity at deployment sites. The economics that make edge AI inevitable.]

The Hardware Stack

[Expand: Raspberry Pi cluster setup. RTSP camera integration. Network topology. Power and thermal management. Why Pi over Jetson or other SBCs — cost per deployment site.]

Squeezing ML Models onto 4GB RAM

[Expand: Model quantization (INT8, pruning). TensorFlow Lite vs ONNX Runtime. Frame sampling strategies — you don't need 30fps inference. The accuracy vs speed trade-off curve.]

RTSP Pipeline Architecture

[Expand: GStreamer pipeline. Frame extraction, preprocessing, inference, post-processing. How you handle multiple camera streams on one Pi. Buffer management and backpressure.]

Cluster Orchestration Without Kubernetes

[Expand: Why K8s is overkill for Pi clusters. Custom orchestration layer. Health checks, failover, OTA updates. How you manage a fleet of Pis remotely.]

Production Lessons from the Field

[Expand: SD card failures, thermal throttling in enclosures, power supply quality matters, network edge cases. The unglamorous reality of edge deployments. What you'd change in v2.]