{
  "id": "stack-profile:edge-ai-iot",
  "_kind": "StackProfile",
  "_file": "domain/stack-profiles/deep-stacks-1.yaml",
  "_cluster": "domain",
  "attributes": {
    "displayName": "Edge AI / IoT Stack (TensorFlow Lite, MQTT, Rust, InfluxDB, Grafana)",
    "description": "A stack for deploying machine learning models on resource-constrained\nedge devices and collecting telemetry from distributed IoT networks.\nTensorFlow Lite (or ONNX Runtime) runs quantized inference models on\nmicrocontrollers and single-board computers with minimal memory and\npower budgets. MQTT provides lightweight pub/sub messaging between\nedge devices and a central broker.\n\nRust is used for performance-critical edge firmware where memory safety\nand zero-cost abstractions matter. InfluxDB stores time-series telemetry\n(sensor readings, inference results, device health) with built-in\ndownsampling and retention policies. Grafana visualizes device fleets,\nmodel accuracy over time, and alert conditions. This stack is ideal\nfor predictive maintenance, smart agriculture, environmental monitoring,\nand industrial quality inspection. The tradeoff is model size constraints\n— quantization and pruning are required to fit models within edge memory.\n",
    "composes": [
      "library:tensorflow",
      "language:rust",
      "tool:grafana",
      "language:python"
    ]
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