Example Concept: Edge inference involves deploying machine learning models on edge devices to process data locally. This reduces the need to send large volumes of data to the cloud for processing, which minimizes latency and conserves bandwidth. By performing inference at the edge, systems can make faster decisions, enhance privacy by keeping data local, and ensure continuous operation even with intermittent internet connectivity.