透過 Azure Stack Hub 讓 AI 位於邊緣 - 中斷連線

HDInsight
Kubernetes Service
Machine Learning
Azure Stack Hub
儲存體
虛擬機器

解決方案構想 Solution Idea

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使用 Azure AI 工具和雲端平臺,新一代的 AI 啟用混合式應用程式可在您的資料所在位置執行。With the Azure AI tools and cloud platform, the next generation of AI-enabled hybrid applications can run where your data lives. 使用 Azure Stack Hub,將定型的 AI 模型帶到邊緣,並將其與您的應用程式整合,以獲得低延遲的智慧,而不需要為本機應用程式進行任何工具或程式變更。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. 有了 Azure Stack Hub,您可以確保雲端解決方案即使在與網際網路中斷連線的情況下仍可運作。With Azure Stack Hub, you can ensure that your cloud solutions work even when disconnected from the internet.

架構Architecture

架構圖表會 下載此架構的SVGArchitecture diagram Download an SVG of this architecture.

資料流程Data Flow

  1. 資料科學家會使用 Azure Machine Learning Studio (傳統) 和 HDInsight 叢集來定型模型。Data scientists train a model using Azure Machine Learning Studio (classic) and an HDInsight cluster. 模型是容器化的,並放入 Azure Container Registry。The model is containerized and put in to an Azure Container Registry.
  2. 此模型會透過圖表中未顯示的步驟部署到 Azure Stack Hub 上的 Kubernetes 叢集。The model is deployed via steps not represented in the diagram to a Kubernetes cluster on Azure Stack Hub.
  3. 終端使用者提供對模型評分的資料。End users provide data that is scored against the model.
  4. 評分中的見解和異常會放置在儲存體中,以供稍後上傳。Insights and anomalies from scoring are placed into storage for later upload.
  5. 全域相關且符合規範的見解可在全域應用程式中取得。Globally-relevant and compliant insights are available in the global app.
  6. 來自邊緣評分的資料會用來改善模型。Data from edge scoring is used to improve the model.

元件Components

後續步驟Next steps