Play 34
Edge AI Deployment
High✅ Ready
On-device inference — ONNX quantization, IoT Hub sync, offline-capable for disconnected environments.
Deploy AI models to edge devices with ONNX quantization, model compression, and offline inference capabilities. IoT Hub manages device fleet, synchronizes model updates, and collects telemetry. Supports disconnected and on-premise environments where cloud connectivity is intermittent or unavailable. Container Instances run inference containers, Azure Monitor tracks fleet health, and automatic rollback protects against bad model pushes.
Architecture Pattern
Edge AI: ONNX quantization, offline inference, fleet management, cloud sync
Azure Services
Azure IoT HubONNX RuntimeContainer InstancesAzure Monitor
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Edge AI Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (109 lines), evaluate (106 lines), tune (104 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with IoT Hub + OpenAI key inputs + envFile
TuneKit (AI Config)
- config/edge.json — quantization level, model config, memory constraints
- config/sync.json — update schedule, rollback rules, fleet targeting
- config/guardrails.json — model validation, inference safety checks
- evaluation/ — inference accuracy, latency benchmarks
Tuning Parameters
Quantization level (INT4/INT8)Sync scheduleFallback configDevice memory budget (2GB→8GB)Model compression ratio
Estimated Cost
Dev/Test
$50–150/mo
Production
$500–2K/mo