AI at the edge with Azure Stack Hub

Container Registry
Kubernetes Service
Machine Learning
Azure Stack Hub
App Service

Solution Idea

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With the Azure AI tools, edge, and cloud platform, edge intelligence is possible. The next generation of AI-enabled hybrid applications can run where your data lives. With Azure Stack Hub, bring a trained AI model to the edge, integrate it with your applications for low-latency intelligence, and continuously feedback into a refined AI model for improved accuracy, with no tool or process changes for local applications.

This solution idea shows a connected Stack Hub scenario, where edge applications are connected to Azure. For the disconnected-edge version of this scenario, see the article AI at the edge - disconnected.


Architecture diagram: AI-enabled application running at the edge with Azure Stack Hub. Download an SVG of this architecture.

Data flow

  1. Data scientists train a model using Azure Machine Learning 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.
  8. (feedback loop) The model retraining can be triggered by a schedule. Data scientists work on the optimization. The improved model is deployed and containerized as an update to the container registry.


Key technologies used to implement this architecture:

  • Azure Machine Learning: Build, deploy, and manage predictive analytics solutions
  • HDInsight: Provision cloud Hadoop, Spark, R Server, HBase, and Storm clusters
  • Container Registry: Store and manage container images across all types of Azure deployments
  • Azure Kubernetes Service (AKS): Simplify the deployment, management, and operations of Kubernetes
  • Storage: Durable, highly available, and massively scalable cloud storage
  • Azure Stack Hub: Build and run innovative hybrid applications across cloud boundaries
  • Azure Functions: Event-driven serverless compute unit for on-demand tasks running without the needs of maintaining the computing server
  • App Service: Path that captures end-user feedback data to enable model optimization

Next steps