This article is a solution idea. If you'd like us to expand the content with more information, such as potential use cases, alternative services, implementation considerations, or pricing guidance, let us know by providing GitHub feedback.
This solution architecture allows you to use edge AI when disconnected from the internet and move your AI models to the edge.
Download a Visio file 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 storage for later upload.
- Globally relevant and compliant insights are available in the global app.
- Data scientists use scoring from the edge to improve the model.
Key technologies used to implement this architecture:
- Azure Machine Learning: Build, deploy, and manage predictive analytics solutions
- HDInsight: Provision cloud Hadoop, Spark, 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
- Virtual Machines: Provision Windows and Linux virtual machines in seconds
- Storage: Durable, highly available, and massively scalable cloud storage
- Azure Stack Hub: Build and run innovative hybrid applications across cloud boundaries
With the Azure AI tools, edge, and cloud platform, edge intelligence is possible. AI-enabled hybrid applications can run where your data lives, on-premises. With Azure Stack Hub, bring a trained AI model to the edge and integrate it with your applications for low-latency intelligence, with no tool or process changes for local applications. With Azure Stack Hub, you can ensure that your cloud solutions work even when disconnected from the internet.
This solution idea shows a disconnected Stack Hub scenario. Issues of latency, intermittent connectivity, or regulations might not always allow for connectivity to Azure. In the disconnected scenario, data is processed locally and later aggregated in Azure for further analytics. For the connected version of this scenario, see the article AI at the edge.
Potential use cases
You might need to deploy as disconnected if you have the following concerns or considerations:
- You have security or other restrictions that require you to deploy Azure Stack Hub in an environment that isn't connected to the internet.
- You want to block data (including usage data) from being sent to Azure.
- You want to use Azure Stack Hub purely as a private cloud solution that's deployed to your corporate intranet, and aren't interested in hybrid scenarios.
- Want to learn more? Check out the related module Introduction to Azure Stack
- 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
See the following product documentation for more information:
- 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
- Virtual Machines documentation
- Azure hybrid and multicloud patterns and solutions documentation
See the following samples to interact with related solutions:
- 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)
See the following related architectures: