Edge AI

Edge AI refers to deploying and running artificial intelligence models directly on edge devices — such as sensors, smartphones, cameras, gateways, and industrial controllers — rather than in centralized cloud servers. By performing on-device inference (and sometimes localized training), Edge AI enables real-time decision-making, lower latency, reduced bandwidth use, and improved privacy. It relies on techniques like model compression, quantization, and specialized accelerators and is used across IoT, autonomous systems, healthcare, retail, and manufacturing. Key challenges include constrained compute and power, secure model deployment, and lifecycle management for updates and monitoring.

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