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Cloud AI

Cloud AI
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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.
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The model is deployed to a Kubernetes cluster on Azure Stack Hub.
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End users provide data that's scored against the model.
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Insights and anomalies from scoring are placed into a queue.
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A function sends compliant data and anomalies to Azure Storage.
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Globally relevant and compliant insights are available in the global app.
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Data from edge scoring is used to improve the model.
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