Training Many Anomaly Detection Models using Azure Batch AI
In the IoT world, it’s not uncommon that you’d want to monitor thousands of devices across different sites to ensure normal behavior. Devices can be as small as microcontrollers or as big as aircraft engines and might have sensors attached to them to collect various types of measurements that are of interest. These measurements often carry signals that indicate whether the devices are functioning as expected or not. Sensor data can be used to train predictive models that serve as alarm systems or device monitors that warn when a malfunction or failure is imminent.
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