Azure Machine Learning : Let’s check our IoT dataset for anomalies!

Introduction

Earlier we setup a basic IoT flow where we captured temperature & humidity and stored it to various outputs. My objective for this week was to create a new flow, that would leverage one of those outputs and do an anomaly detection on the data received. As this detection might take some time, I did not want to do this “in-line” with my current flow. So I’ve added a new one… which kinda looks like this.

2017-01-05-15_38_29-job-diagram-microsoft-azure

The details of the Machine Learning part in combination with Stream Analytics will be for another post. This as I’m still struggling a bit to get it full operational. 😉 So today we’ll “just” cover the Machine Learning aspect of the flow.

 

Disclaimer

To be very clear up front… I’m by no means an expert at machine learning / big data / etc. In my quest to learn, I played around with the Machine Learning Studio of Azure, where I would like to share  my experience on this. 😉

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