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Disconnected AI at the edge with Azure Stack Hub

Container Registry
HDInsight
Kubernetes Service
Machine Learning
Azure Stack Hub

Solution ideas

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.

Architecture

Architecture diagram: AI-enabled application running at the edge with Azure Stack Hub and hybrid connectivity.

Download a Visio file of this architecture.

Dataflow

  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 storage for later upload.
  5. Globally relevant and compliant insights are available in the global app.
  6. Data scientists use scoring from the edge to improve the model.

Components

Key technologies used to implement this architecture:

Scenario details

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.

Next steps

See the following product documentation for more information:

See the following samples to interact with related solutions:

See the following related architectures: