Running inference at the edge dramatically reduces the latency between data capture and decision, which is essential for use cases like computer vision, predictive maintenance, and personalized recommendations. It also keeps training data local when needed for privacy or compliance reasons. Many enterprises now run a hybrid pattern where models are trained centrally and deployed for inference at the edge, getting the best of both architectures.
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