Build Interpretable AI for Safety-Critical IoT with the Raspberry Pi 5 SHAP Anomaly Detection Kit

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

When an IoT anomaly detector flags a temperature spike in a pharmaceutical cold chain or a pressure fluctuation in an industrial boiler, you need to know exactly which sensor features drove that alert — not a black-box guess. This kit puts SHAP (SHapley Additive exPlanations) on a Raspberry Pi 5, letting you train a neural network on multi-sensor data from three ESP32 nodes, then generate per-alert explanations that engineers and auditors can trust. It turns the Pi 5 into an edge explainability engine for safety-critical IoT, mirroring the interpretable AI workflows demanded in industrial compliance and research.

What You'll Build

You will create a three-node IoT sensor mesh using ESP32 boards streaming temperature, humidity, gas, and vibration data to a Pi 5. On that Pi, you’ll train a lightweight autoencoder-based anomaly detector, then integrate SHAP to explain every alert in terms of individual sensor contributions. The result is a dashboard-like CLI report showing, for each anomaly, a ranked list of the top sensor features that pushed the model to trigger an alert — exactly what is needed for root-cause analysis in domains like cold storage auditing, machinery health monitoring, or environmental hazard detection.

What You'll Learn

• Deploy TensorFlow Lite models on Raspberry Pi 5 with NVMe acceleration for real-time inference
• Implement SHAP explainability on edge devices to extract per-feature contributions from anomaly scores
• Build a multi-sensor IoT pipeline using ESP32, MQTT, and time-series preprocessing
• Apply interpretable AI principles to safety-critical systems, preparing for research or industry roles where model transparency is mandatory

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit fits B.Tech ECE/EEE students tackling final-year projects on explainable AI or industrial IoT, Smart India Hackathon participants building safety-critical solutions, and researchers at IITs, NITs, or VIT who need a reproducible edge AI testbed. It is also ideal for ATL Tinkering Lab mentors wanting an advanced, real-world AI interpretability demonstration for senior students. If you’ve completed basic Python and microcontroller courses and want to advance into interpretable machine learning on the edge, this is your project.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.