AWS security-lake: Fix broken URLs in SageMaker integration documentation
Summary
Corrected double slashes in URLs to ensure proper linking to SageMaker and AWS blog resources.
Security assessment
The change resolves URL syntax issues without introducing or modifying security-related content.
Evidence
+[Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) is a fully managed machine learning (ML) service. With Security Lake, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker AI ML tools available across multiple integrated development environments (IDEs).
Diff
diff --git a/security-lake/latest/userguide/sagemaker-integration.md b/security-lake/latest/userguide/sagemaker-integration.md index d6f4c3f12..9111acdf8 100644 --- a//security-lake/latest/userguide/sagemaker-integration.md +++ b//security-lake/latest/userguide/sagemaker-integration.md @@ -13 +13 @@ SageMaker AI insights -[Amazon SageMaker AI](https://docs.aws.amazon.com//sagemaker/latest/dg/whatis.html) is a fully managed machine learning (ML) service. With Security Lake, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker AI ML tools available across multiple integrated development environments (IDEs). +[Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) is a fully managed machine learning (ML) service. With Security Lake, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker AI ML tools available across multiple integrated development environments (IDEs). @@ -17 +17 @@ SageMaker AI insights -You can generate machine learning insights for Security Lake by using SageMaker AI Studio. This Studio is a web integrated development environment (IDE) for machine learning that provides tools for data scientists to prepare, build, train, and deploy machine learning models. With this solution, you can quickly deploy a base set of Python notebooks focusing on [AWS Security Hub CSPM](https://docs.aws.amazon.com/securityhub/latest/userguide/what-is-securityhub.html) findings in Security Lake, which can also be expanded to incorporate other AWS sources or custom data sources in Security Lake. For more details, see [Generate machine learning insights for Amazon Security Lake data using Amazon SageMaker AI](https://aws.amazon.com/blogs//security/generate-machine-learning-insights-for-amazon-security-lake-data-using-amazon-sagemaker/). +You can generate machine learning insights for Security Lake by using SageMaker AI Studio. This Studio is a web integrated development environment (IDE) for machine learning that provides tools for data scientists to prepare, build, train, and deploy machine learning models. With this solution, you can quickly deploy a base set of Python notebooks focusing on [AWS Security Hub CSPM](https://docs.aws.amazon.com/securityhub/latest/userguide/what-is-securityhub.html) findings in Security Lake, which can also be expanded to incorporate other AWS sources or custom data sources in Security Lake. For more details, see [Generate machine learning insights for Amazon Security Lake data using Amazon SageMaker AI](https://aws.amazon.com/blogs/security/generate-machine-learning-insights-for-amazon-security-lake-data-using-amazon-sagemaker/).