Modern IoT systems are no longer just “devices sending data to the cloud.” To deliver real-time insights, anomaly detection, and closed-loop control, we increasingly need intelligence at the edge—right where data is generated.

In this session, I’ll walk through how to design and build an IoT architecture that pushes analytics and AI/ML to the edge while still integrating cleanly with cloud services. Drawing on production experience from industrial IoT, smart buildings, and edge AI platforms, we’ll cover:

How to structure an IoT stack from devices → edge gateways → cloud

When and why to move analytics and ML inference to the edge

Practical patterns for messaging (MQTT, pub/sub), streaming, and command/control

Designing for reliability: offline operation, reconnection, and buffering

Security basics for IoT (device identity, certificates, secure update paths)

Real examples of using edge intelligence for predictive maintenance and anomaly detection

The talk will be vendor-neutral and code-light, focusing on architecture, patterns, and trade-offs that software engineers and architects can take back to their own IoT or edge projects.