Back in 2018, the Azure Stream Analytics team announced Anomaly detection for Azure Stream Analytics. And, it was also supported on the Azure IoT Edge. In November 2018, I was allowed to demonstrate this at the SPS IPC Drives in Nuremberg:
Since then, anomaly detection has become a first citizen of Azure Stream Analytics.
Azure Stream Analytics Anomaly Detection is able to ‘automatically’ detect spikes, dips, and trends in a stream of values. This is based on math and all you need to do is to specify how many values you expect and how sensitive the detection must be. It is in fact Machine Learning, in the end. So it is a prediction with a certain certainty…
And it’s just part of the Stream Analytics query language. So on the edge, we can deploy a Stream Analytics module (the engine of stream analytics). This is a fixed module from Microsoft.
All you have to do is feeding it a query, inputs, outputs and, user-defined functions if available:
We can define all this in the cloud, inside a stream analytics job and we can even test it. Those parts are then packed as a blob and put in blob storage. The Stream Analytics job can then download it and run it.
The inputs and outputs of the Stream Analytics job can be attached to the normal Azure IoT Edge routing mechanism.
Let’s see how this is set up:Doorgaan met het lezen van “Azure Stream Analytics anomaly detection on the edge”