AI at the Edge - Disconnected

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, 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, you can ensure that your cloud solutions work even when disconnected from the internet.

Intelligence artificielle à la périphérie hors connexionGrâce aux outils d’intelligence artificielle et à la plateforme cloud Azure, les applications hybrides de nouvelle génération utilisant l’intelligence artificielle peuvent s’exécuter là où vos données résident. Azure Stack vous permet d’apporter un modèle d’intelligence artificielle formé à la périphérie et de l’intégrer avec vos applications afin de disposer d’une intelligence à faible latence, sans nécessité de modifier des outils ou des processus pour les applications locales. Avec Azure Stack, vous pouvez vous assurer que vos solutions cloud fonctionnent même si elles sont déconnectées d’Internet.654321

Data scientists train a model using Azure Machine Learning and an HDInsight cluster. The model is containerized and put in to an Azure Container Registry.

The model is deployed via an offline installer to a Kubernetes cluster on Azure Stack.

End users provide data that is 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 from edge scoring is used to improve the model.

  1. 1 Data scientists train a model using Azure Machine Learning and an HDInsight cluster. The model is containerized and put in to an Azure Container Registry.
  2. 2 The model is deployed via an offline installer to a Kubernetes cluster on Azure Stack.
  3. 3 End users provide data that is scored against the model.
  1. 4 Insights and anomalies from scoring are placed into storage for later upload.
  2. 5 Globally-relevant and compliant insights are available in the global app.
  3. 6 Data from edge scoring is used to improve the model.

Implementation guidance

Products/Description Documentation

HDInsight

Provision cloud Hadoop, Spark, R Server, HBase, and Storm clusters

Machine Learning Studio

Easily build, deploy, and manage predictive analytics solutions

Virtual Machines

Provision Windows and Linux virtual machines in seconds

Azure Kubernetes Service (AKS)

Simplify the deployment, management, and operations of Kubernetes

Storage

Durable, highly available, and massively scalable cloud storage

Azure Stack

Build and run innovative hybrid applications across cloud boundaries