Cloud AI

Cloud AI

Data scientists train a model using Azure Machine Learning workbench and an HDInsight cluster. The model is containerized and put into an Azure Container Registry.
The model is deployed to a Kubernetes cluster on Azure Stack Hub.
End users provide data that's scored against the model.
Insights and anomalies from scoring are placed into a queue.
A function sends compliant data and anomalies to Azure Storage.
Globally relevant and compliant insights are available in the global app.
Data from edge scoring is used to improve the model.