Edge AI / IoT Stack (TensorFlow Lite, MQTT, Rust, InfluxDB, Grafana) overview

A stack for deploying machine learning models on resource-constrained edge devices and collecting telemetry from distributed IoT networks. TensorFlow Lite (or ONNX Runtime) runs quantized inference models on microcontrollers and single-board computers with minimal memory and power budgets. MQTT provides lightweight pub/sub messaging between edge devices and a central broker. Rust is used for performance-critical edge firmware where memory safety and zero-cost abstractions matter. InfluxDB stores time-series telemetry (sensor readings, inference results, device health) with built-in downsampling and retention policies. Grafana visualizes device fleets, model accuracy over time, and alert conditions. This stack is ideal for predictive maintenance, smart agriculture, environmental monitoring, and industrial quality inspection. The tradeoff is model size constraints — quantization and pruning are required to fit models within edge memory.