edge AI deployment 2

https://astro-wiki.win/index.php/Why_the_AMD_Instinct_MI450_Is_Gaining_Ground_in_Accelerated_Computing

Deploying edge AI means running machine learning models directly on local devices like cameras, sensors, or phones instead of in the cloud. This reduces latency, cuts bandwidth use, and improves privacy since data doesn’t need to travel elsewhere. It’s especially useful in real-time applications like autonomous vehicles or factory automation, where speed and reliability matter. However, it requires optimizing models to run efficiently on hardware with limited computing power and memory.