AWS security-lake documentation change
Summary
Modified SageMaker documentation links and blog URL formatting
Security assessment
URL path adjustments and blog link formatting changes without any modifications to security controls, vulnerabilities, or security feature descriptions. Existing security context about Security Hub findings remains unchanged.
Diff
diff --git a/security-lake/latest/userguide/sagemaker-integration.md b/security-lake/latest/userguide/sagemaker-integration.md index fa065ffb2..38a6e61b8 100644 --- a//security-lake/latest/userguide/sagemaker-integration.md +++ b//security-lake/latest/userguide/sagemaker-integration.md @@ -11 +11 @@ 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). @@ -15 +15 @@ 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](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](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/).