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Real Time Sensor Anomaly Detection with Sci Kit Learn and the Azure Stack


The introduction of IoT and Big Data has disrupted the multi-billion dollar municipal water management industry. However, sensors sometimes malfunction and differentiating between sensor error and expected anomalous readings from events such as storms and floods can be extremely difficult. Traditionally to account for irregularities, municipalities hire analysts to manually sift through sensor data and modify values believed to be caused by sensor error, an extremely costly and error prone process. Recently the Microsoft Partner Catalyst team partnered with the industry to build an anomaly detection model to differentiate between irregular sensor readings and sensor error, and put the model into production using Sci-Kit Learn as well as Azure Event Hubs, Stream Analytics and PowerBI. In this session participants will receive a high level overview of the the sensor error detection problem, and learn how to build a production visualization pipeline for classification models in near real time for their own use.


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