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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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Smart infrastructure
1.
Infrastructure is supposed
to be BORING.
2.
It is not
supposed to hit headlines.
3.
Unfortunately, infrastructure is
DUMB. It can not SEE. It can not ADAPT to changes. It does not LEARN from its mistakes.
4.
What if, we
make your infrastructure SMART, so that It can SEE through sensors. It can ADAPT to changes with REAL TIME CONTROLS. It can LEARN from its mistakes using models and data.
5.
SEE LEARN ADAPT DATA & INSTRUMENTATION MODELS &
TOOLS Hydrology Hydraulics Water Quality REAL TIME OPTIMIZATION TOOL
6.
So your Infrastructure
can stay BORING.
Download now