AWS sagemaker documentation change
Summary
Updated grammar, clarified admin responsibilities, and changed section title from 'Cluster observability' to 'Cluster and task observability'
Security assessment
Changes focus on grammatical improvements and expanding observability scope to include tasks, but no direct security vulnerability fixes or new security features introduced.
Diff
diff --git a/sagemaker/latest/dg/sagemaker-hyperpod-eks-cluster-observability-model.md b/sagemaker/latest/dg/sagemaker-hyperpod-eks-cluster-observability-model.md index 193f119db..13324dd2b 100644 --- a//sagemaker/latest/dg/sagemaker-hyperpod-eks-cluster-observability-model.md +++ b//sagemaker/latest/dg/sagemaker-hyperpod-eks-cluster-observability-model.md @@ -7 +7 @@ -SageMaker HyperPod clusters orchestrated with Amazon EKS can integrate with the [MLflow application on Amazon SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html). Cluster admins set up the MLflow server and connect it with the SageMaker HyperPod clusters. Data scientists can gain insights into the model +SageMaker HyperPod clusters orchestrated with Amazon EKS can integrate with the [MLflow application on Amazon SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow.html). Cluster admins set up the MLflow server and connect it with the SageMaker HyperPod clusters. Data scientists can gain insights into the model. @@ -11 +11 @@ SageMaker HyperPod clusters orchestrated with Amazon EKS can integrate with the -An MLflow tracking server should be created by cluster admin. +A cluster admin must create an MLflow tracking server. @@ -121 +121 @@ Run the following code to create the role and attach the trust relationship. -The ARNs should be the one from the MLflow server and the S3 bucket set up with the MLflow server during the server you created following the instructions [Set up MLflow infrastructure](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-cli.html#mlflow-create-tracking-server-cli-infra-setup). +The ARNs are the one from the MLflow server and the S3 bucket set up with the MLflow server during the server you created following the instructions [Set up MLflow infrastructure](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-create-tracking-server-cli.html#mlflow-create-tracking-server-cli-infra-setup). @@ -172 +172 @@ Data scientists need to set up the training script and docker image to emit metr -Make sure that you add installation of mlflow and sagemaker-mlflow packages in the Docker file. To learn more about the installation of the packages, requirements, and compatilible versions of the packages, see [Install MLflow and the SageMaker AI MLflow plugin](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-track-experiments.html#mlflow-track-experiments-install-plugin). +Make sure that you add installation of mlflow and sagemaker-mlflow packages in the Docker file. To learn more about the installation of the packages, requirements, and compatible versions of the packages, see [Install MLflow and the SageMaker AI MLflow plugin](https://docs.aws.amazon.com/sagemaker/latest/dg/mlflow-track-experiments.html#mlflow-track-experiments-install-plugin). @@ -244 +244 @@ Observability -Cluster observability +Cluster and task observability