Although they are two different technological ideas, edge AI and embedded AI are closely related. By directly implementing AI algorithms on edge devices, edge AI allows for real-time data processing without significantly depending on cloud infrastructure. Applications with limited bandwidth and latency, such as smart cameras or driverless cars, are perfect for it.

On the other hand, embedded AI describes the incorporation of AI capabilities into embedded systems, which are usually devices with limited resources, such as microcontrollers. It focuses on maximizing AI's performance on the least amount of hardware, which makes it appropriate for industrial automation and Internet of Things devices.

To learn more, check out The IoT Academy's AI and Embedded System courses, which are designed for practical experience and professional preparedness.
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