What makes IoT Edge suitable for running machine learning applications?

IoT Edge is particularly suitable for running machine learning applications because it enables the training of models in the cloud, where resources and data are typically more abundant, and then allows those models to be deployed on Edge devices. This is ideal for scenarios where low latency is crucial, or where continuous data streaming from devices might incur high costs or require substantial bandwidth if sent to the cloud for processing.

This deployment model leverages the strengths of both the cloud and edge computing: the cloud can handle intensive computational tasks such as training on large datasets while the Edge can perform inference—making real-time decisions and processing data locally without always needing to communicate back to the cloud. This setup not only enhances performance but also improves reliability and response times, as the system can function even with intermittent connectivity. Therefore, the ability to train models in the cloud and deploy them locally is a defining characteristic that underlines IoT Edge's suitability for machine learning applications.