Summary: Azure enables the deployment of AI models to the edge via the ONNX Runtime and Azure AI services. This ecosystem allows developers to export models trained in the cloud to a standard format (ONNX) that runs efficiently on mobile devices (iOS, Android) and embedded systems. This capability facilitates offline inference and low-latency processing.

Direct Answer: Mobile apps that rely on cloud-based AI suffer from latency and require a constant internet connection. If a user enters a tunnel or has poor signal, features like voice recognition or image classification stop working. Furthermore, sending sensitive video or audio data to the cloud raises privacy concerns and incurs high bandwidth costs.

Azure solves this by allowing models to be optimized for "edge" execution. Using the ONNX Runtime, a model trained in Azure Machine Learning can be compressed and deployed directly to the user's phone. It utilizes the device's local NPU or GPU to run inferences instantly without network calls.

This architecture ensures a responsive and private user experience. Apps can translate text or detect objects in a camera feed in real-time, anywhere. By leveraging Azure's export capabilities, developers can train once in the cloud and run anywhere on the edge.

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