本文為英文版的機器翻譯版本，如內容有任何歧義或不一致之處，概以英文版為準。

# 後續步驟和資源
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本指南會逐步引導您在規劃要帶入生產環境的機器學習模型生命週期時，了解一些考量事項。它討論了四個領域的挑戰和最佳實務，包括資料、訓練、部署和監控，並包含其他相關的資源。

AWS 提供 Well-Architected 架構，可協助雲端架構師為各種應用程式、工作負載和技術網域建置安全、高效能、彈性且高效率的基礎設施。如需其他閱讀，請參閱 AWS Well-Architected 提供的 [Machine Learning Lens](https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/machine-learning-lens.html)。

## 資源
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**Amazon SageMaker AI 文件**
+ [Amazon SageMaker AI 功能商店](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-getting-started.html)
+ [功能存放區安全性和存取控制](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-security.html)
+ [Shapley 值](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-shapley-values.html)
+ [Amazon SageMaker AI Debugger](https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html)
+ [Amazon SageMaker AI 管道](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html)
+ [Amazon SageMaker AI 預設專案範本](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects-templates-sm.html)
+ [SageMaker AI 即時推論](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html)
+ [自動擴展 Amazon SageMaker AI 模型](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html)
+ [Amazon SageMaker AI 非同步推論](https://docs.aws.amazon.com/sagemaker/latest/dg/async-inference.html)
+ [SageMaker AI 模型監控](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html)

**AWS 開發人員工具**
+ [AWS CodePipeline](https://aws.amazon.com/codepipeline/)

**AWS 部落格文章**
+ [了解 Amazon SageMaker AI Feature Store 的主要功能](https://aws.amazon.com/blogs/machine-learning/understanding-the-key-capabilities-of-amazon-sagemaker-feature-store/)
+ [使用 PyDeequ 大規模測試資料品質](https://aws.amazon.com/blogs/big-data/testing-data-quality-at-scale-with-pydeequ/)
+ [Amazon SageMaker AI 實驗](https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/)
+ [使用 CodePipeline 和 安全地部署和監控 Amazon SageMaker 端點 AWS CodeDeploy](https://aws.amazon.com/blogs/machine-learning/safely-deploying-and-monitoring-amazon-sagemaker-endpoints-with-aws-codepipeline-and-aws-codedeploy/)
+ [在 Amazon SageMaker AI 中部署影子 ML 模型](https://aws.amazon.com/blogs/machine-learning/deploy-shadow-ml-models-in-amazon-sagemaker/)
+ [使用 Amazon SageMaker AI 在生產環境中測試 ML 模型的 A/B](https://aws.amazon.com/blogs/machine-learning/a-b-testing-ml-models-in-production-using-amazon-sagemaker/)