Todays post will be the backend tour of “Frietjes-of-Niet” (translated from Dutch : “French-Frites-or-Not?”). A big part of the mission of Azure is about democratizing technology so it becomes accessible to organizations in order for them to achieve more. AI (Artificial Intelligence) is a key part of that vision.
What will be the flow for today?
- We’ll train a model to recognize fries
- Next we’ll be exporting that model to be used as a container
- Afterwards we’ll build that container
- To end with deploying (and testing) it onto AKS
Sound cool? Let’s get to it..
Continue reading “Azure Custom Vision AI : From training to deploying the container export on the Azure Kuberenetes Service (AKS)”
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.
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.
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. 😉
Continue reading “Azure Machine Learning : Let’s check our IoT dataset for anomalies!”