Machine Learning @ Edge

Machine learning at the edge refers to the use of machine learning algorithms and models on devices that are located at the edge of a network, rather than in the cloud or a centralized data center. By performing machine learning at the edge, data can be processed and analyzed closer to the source, which can reduce latency, improve privacy, and reduce the amount of data that needs to be transmitted over the network. Machine learning at the edge is particularly useful in scenarios where real-time analysis is required or where there is a need to process data in a distributed or offline setting.

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