diarizationThe predictive/prescriptive AI life cycle of a device starts with data collection design. Data is analyzed for factors such as correlation and variance. Then the devices start being manufactured. Other than a small number of sample devices, there is usually no device failures, that produce machine learning models. To compensate for this, most manufacturers use duty cycle thresholds to determine whether a device is in a good state or a bad state. These duty cycle standards may be that that the device is running too hot or an arbitrary value is put on a sensor for an alert. But the data quickly needs more advanced analysis. The sheer volume of data can be daunting for an individual. The analyst needs to look through ...
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