How can I build a repeatable ML workflow in SageMaker?
如何在 SageMaker 中建置可重複的 ML 工作流程？
Amazon SageMaker Pipelines  helps you create fully automated ML workflows from data preparation through model deployment so you can scale to thousands of ML models in production. You can create Pipelines with the SageMaker Python SDK and view, execute, audit them from the visual interface of the SageMaker Studio. SageMaker Pipelines takes care of managing data between steps, packaging the code recipes, and orchestrating their execution, reducing months of coding to a few hours. Every time a workflow executes, a complete record of the data processed and actions taken is kept so data scientists and ML developers can quickly debug problems.
Amazon SageMaker Pipelines 可協助您建立從資料準備至模型部署的全自動 ML 工作流程，這樣您就可以在生產中擴展至數千個 ML 模型。您可以使用 SageMaker Python SDK 建構管道，並從 SageMaker Studio 的視覺化介面檢視、執行和稽核這些管道。SageMaker Pipelines 負責管理步驟之間的資料，封裝程式碼配方，並協調其執行，將數月的編碼時間縮短到幾小時。每次工作流程執行時，都會保留處理的資料和採取的動作的完整記錄，以便資料科學家和 ML 開發人員可以快速地對問題偵錯。