AWS wellarchitected documentation change
Summary
Fixed formatting of colons in two sections: 'Secure ML environment' and 'Continuous delivery phase' headings
Security assessment
Purely cosmetic formatting changes (spacing after colon). No security content was added, removed, or modified in the actual text about ML security requirements.
Diff
diff --git a/wellarchitected/latest/financial-services-industry-lens/artificial-intelligence-and-machine-learning.md b/wellarchitected/latest/financial-services-industry-lens/artificial-intelligence-and-machine-learning.md index e9cd457c3..54b2454dd 100644 --- a//wellarchitected/latest/financial-services-industry-lens/artificial-intelligence-and-machine-learning.md +++ b//wellarchitected/latest/financial-services-industry-lens/artificial-intelligence-and-machine-learning.md @@ -13 +13 @@ Integration of AI/ ML technologies into day-to-day operations has advanced slowl - * **Secure ML environment** : Financial institutions have stringent security requirements for several reasons, including data protection, regulatory compliance, prevention of adversarial exploits, and to maintain trust and responsible use of AI. + * **Secure ML environment:** Financial institutions have stringent security requirements for several reasons, including data protection, regulatory compliance, prevention of adversarial exploits, and to maintain trust and responsible use of AI. @@ -48 +48 @@ _Figure 2: Reference architecture for an AI/ML pipeline_ -**Continuous delivery phase** : SageMaker AI MLOps capability automates deploying and delivering machine learning models into production in a consistent manner. ML operations teams can leverage AWS continuous integration capabilities using CloudFormation, CodeBuild, and CodeDeploy to automate model deployment workflows. Amazon SageMaker AI model monitoring allows customers to monitor ML applications for potential data drifts, model drifts, and bias drifts. +**Continuous delivery phase:** SageMaker AI MLOps capability automates deploying and delivering machine learning models into production in a consistent manner. ML operations teams can leverage AWS continuous integration capabilities using CloudFormation, CodeBuild, and CodeDeploy to automate model deployment workflows. Amazon SageMaker AI model monitoring allows customers to monitor ML applications for potential data drifts, model drifts, and bias drifts.