AI is not confined to the cloud — it increasingly lives in the devices around us. This episode introduces edge AI, where models run locally on Internet of Things (IoT) devices. Benefits include lower latency, improved privacy, and functionality even without network connections. We’ll explain how embedded machine learning models are compressed and optimized for limited hardware, and how techniques like federated learning allow devices to contribute to training without centralizing sensitive data.

Examples bring the concept to life: smart home assistants, wearable health monitors, autonomous vehicles, and industrial IoT sensors all rely on local AI. We also discuss challenges such as power consumption, interoperability across vendors, and security vulnerabilities in connected devices. As AI becomes embedded in millions of physical objects, understanding edge and IoT deployments is key to recognizing where intelligence is heading — not just in data centers, but in our daily environments. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.