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With the Azure AI tools and cloud platform, 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 and integrate it with your applications for low-latency intelligence, with no tool or process changes for local applications.
Download an SVG of this architecture.
- 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.
- 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.
- 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