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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.
Download an SVG of this architecture.
- Data scientists train a model using Azure Machine Learning 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.
- (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
- Want to learn more? Check out the related Learn pathway: Microsoft Learn Azure Stack Module
- Get Microsoft Certified for Azure Stack Hub with the Azure Stack Hub Operator Associate certification
- How to install the AKS Engine on Linux in Azure Stack Hub
- How to install the AKS Engine on Windows in Azure Stack Hub
- Deploy your ML models to an edge device with Azure Stack Edge Devices
- Innovate further and deploy Azure Cognitive Services (Speech, Language, Decision, Vision) containers to Azure Stack Hub
- App Service documentation
- Azure Kubernetes Service (AKS) documentation
- Azure Machine Learning documentation
- Azure Stack Hub documentation
- Azure Stack Hub Deployment Options
- Container Registry documentation
- HDInsight documentation
- Storage documentation
- AKS Engine on Azure Stack Hub (on GitHub)
- Azure Samples - Edge Intelligence on Azure Stack Hub (on GitHub)
- Azure Samples -Azure Stack Hub Foundation (on GitHub)
- Azure hybrid and multicloud patterns and solutions documentation