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Cloud AI

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Cloud AI

  1. 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.

  2. The model is deployed to a Kubernetes cluster on Azure Stack Hub.

  3. End users provide data that's scored against the model.

  4. Insights and anomalies from scoring are placed into a queue.

  5. A function sends compliant data and anomalies to Azure Storage.

  6. Globally relevant and compliant insights are available in the global app.

  7. Data from edge scoring is used to improve the model.

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