本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

# 后续步骤和资源
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本指南将引导您在规划要投入生产的机器学习模型的生命周期时需要考虑的一些注意事项。它讨论了数据、培训、部署和监控这四个领域的挑战和最佳实践，并包括其他相关资源。

AWS 提供 Well-Architected Framework，可帮助云架构师为各种应用程序、工作负载和技术领域构建安全、高性能、弹性和高效的基础架构。欲了解更多内容，请参阅 Well-Architecte AWS d 提供的[机器学习镜头](https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/machine-learning-lens.html)。

## 资源
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**亚马逊 SageMaker AI 文档**
+ [亚马逊 SageMaker AI 专题商店](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-getting-started.html)
+ [功能库安全和访问控制](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-security.html)
+ [Shapley 的价值观](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-shapley-values.html)
+ [亚马逊 A SageMaker I 调试器](https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html)
+ [亚马逊 SageMaker AI 管道](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html)
+ [亚马逊 SageMaker AI 默认项目模板](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects-templates-sm.html)
+ [SageMaker AI 实时推理](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html)
+ [自动缩放 Amazon SageMaker 人工智能模型](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html)
+ [亚马逊 SageMaker AI 异步推理](https://docs.aws.amazon.com/sagemaker/latest/dg/async-inference.html)
+ [SageMaker AI 模型监视器](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html)

**AWS 开发者工具**
+ [AWS CodePipeline](https://aws.amazon.com/codepipeline/)

**AWS 博客文章**
+ [了解 Amazon A SageMaker I Feature Store 的关键功能](https://aws.amazon.com/blogs/machine-learning/understanding-the-key-capabilities-of-amazon-sagemaker-feature-store/)
+ [使用以下方法大规模测试数据质量 PyDeequ](https://aws.amazon.com/blogs/big-data/testing-data-quality-at-scale-with-pydeequ/)
+ [亚马逊 SageMaker AI 实验](https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/)
+ [使用和安全部署和监控 Amazon SageMaker 终端 CodePipeline 节点 AWS CodeDeploy](https://aws.amazon.com/blogs/machine-learning/safely-deploying-and-monitoring-amazon-sagemaker-endpoints-with-aws-codepipeline-and-aws-codedeploy/)
+ [在 Amazon A SageMaker I 中部署影子机器学习模型](https://aws.amazon.com/blogs/machine-learning/deploy-shadow-ml-models-in-amazon-sagemaker/)
+ [A/B 使用 Amazon A SageMaker I 在生产环境中测试 ML 模型](https://aws.amazon.com/blogs/machine-learning/a-b-testing-ml-models-in-production-using-amazon-sagemaker/)