Build a Production-Ready IoT Drift Detector with Raspberry Pi 5

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

Sensor data distributions shift constantly in real-world IoT deployments, silently degrading static machine learning models. This kit lets you build a complete ADWIN-based drift detector on a Raspberry Pi 5 that continuously monitors incoming streams and triggers model retraining the moment a concept drift is detected — the same pattern used in production financial fraud detection and industrial predictive maintenance systems.

What You'll Build

You will assemble and program a distributed edge AI system. A central Raspberry Pi 5 with NVMe SSD serves as the data logger and drift detector, running the ADWIN algorithm over sensor streams. Three ESP32 nodes transmit temperature, humidity, pressure, and other readings via MQTT. When the statistical distribution of any sensor changes beyond a configurable threshold, the Pi 5 automatically initiates a model retraining pipeline on the logged data, restoring accuracy without manual oversight. The final setup is a robust, production-grade monitoring loop that you can adapt to any supervised ML project.

What You'll Learn

• Implementing the ADWIN adaptive windowing algorithm for real-time drift detection on resource-constrained hardware
• Configuring a high-speed NVMe SSD on Pi 5 via M.2 HAT+ for low-latency time-series storage
• Building a multi-node IoT sensor mesh with ESP32 boards, MQTT, and custom payload formats
• Designing an event-driven retraining trigger that seamlessly integrates with scikit-learn pipelines

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

This kit is built for advanced B.Tech ECE/EEE final-year students, M.Tech IoT researchers, and Smart India Hackathon teams who need a working concept drift detector embedded in a real sensor network. It’s equally valuable for industry interns prototyping reliable ML pipelines at IIT, NIT, VIT, or BITS campuses, where demonstrating model resilience under distribution shift can set a project apart.

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.