Which components of SageMaker can be added to SageMaker Pipelines?
可以將 SageMaker 的哪些元件新增至 SageMaker Pipelines？
A SageMaker Pipeline is composed of ‘steps’. You can choose any of the natively supported step types to compose a workflow that invokes various SageMaker features (eg. training, evaluation) or other AWS services (eg. EMR, Lambda). You can also lift-and-shift your existing ML Python code into SageMaker Pipeline by either using the ‘@step’ python decorator or adding entire python Notebooks as components of the Pipeline. For additional details, please refer to the SageMaker Pipelines developer guide.
SageMaker Pipeline 由「步驟」組成。您可以選擇任何原生支援的步驟類型來構建調用各種 SageMaker 功能 (例如訓練、評估) 或其他 AWS 服務 (例如 EMR、Lambda) 的工作流程。您也可以使用「@step」Python 裝飾項目或將整個 Python 筆記本新增為 Pipeline 組件，將現有 ML Python 程式碼移到 SageMaker Pipeline。如需更多詳細資訊，請參閱「SageMaker Pipelines 開發人員指南」。