AWS prescriptive-guidance documentation change
Summary
Updated author attribution and fixed quotation marks in documentation reference
Security assessment
Changes involve typo corrections and formatting fixes (curly quotes to straight quotes) without modifying security-related content or introducing new security features.
Diff
diff --git a/prescriptive-guidance/latest/patterns/streamline-machine-learning-workflows-by-using-amazon-sagemaker.md b/prescriptive-guidance/latest/patterns/streamline-machine-learning-workflows-by-using-amazon-sagemaker.md index 51aef4c50..f9e21ff8f 100644 --- a//prescriptive-guidance/latest/patterns/streamline-machine-learning-workflows-by-using-amazon-sagemaker.md +++ b//prescriptive-guidance/latest/patterns/streamline-machine-learning-workflows-by-using-amazon-sagemaker.md @@ -9 +9 @@ SummaryPrerequisites and limitationsArchitectureToolsBest practicesEpicsTroubles - _Created by David Sauerwein (AWS), Julian Ferdinand Grueber (AWS), and Marco Geiger (AWS)_ + _David Sauerwein, Marco Geiger, and Julian Ferdinand Grueber, Amazon Web Services_ @@ -59 +59 @@ The diagram shows the following workflow: - 2. Once satisfied with the algorithm, the data scientist builds and pushes the Docker image to the Amazon Elastic Container Registry (Amazon ECR) repository named `hydra-sm-artifact`. (For more details, see “Run workflows on SageMaker AI” in Epics.) + 2. Once satisfied with the algorithm, the data scientist builds and pushes the Docker image to the Amazon Elastic Container Registry (Amazon ECR) repository named `hydra-sm-artifact`. (For more details, see "Run workflows on SageMaker AI" in Epics.)