AWS sagemaker documentation change
Summary
Consistently updated 'AWS CloudFormation' to 'CloudFormation' throughout EMR quickstart guide
Security assessment
Purely branding/nomenclature change. All operational instructions (including authentication methods and cleanup procedures) remain unchanged with no security enhancements or vulnerability mitigations introduced.
Diff
diff --git a/sagemaker/latest/dg/studio-notebooks-emr-cluster-quickstart.md b/sagemaker/latest/dg/studio-notebooks-emr-cluster-quickstart.md index 4d5880a79..18d467668 100644 --- a//sagemaker/latest/dg/studio-notebooks-emr-cluster-quickstart.md +++ b//sagemaker/latest/dg/studio-notebooks-emr-cluster-quickstart.md @@ -23 +23 @@ To get started, sign in to the AWS Management Console using an AWS Identity and - * Step 4: Clean up your AWS CloudFormation stack + * Step 4: Clean up your CloudFormation stack @@ -30 +30 @@ To get started, sign in to the AWS Management Console using an AWS Identity and -In the following steps, you apply a AWS CloudFormation stack to automatically create a new SageMaker AI domain. The stack also creates a user profile and configures the needed environment and permissions. The SageMaker AI domain is configured to let you directly launch Amazon EMR clusters from Studio. For this example, the Amazon EMR clusters are created in the same AWS account as SageMaker AI without authentication. You can find additional AWS CloudFormation stacks supporting various authentication methods like Kerberos in the [getting_started](https://github.com/aws-samples/sagemaker-studio-emr/tree/main/cloudformation/getting_started) GitHub repository. +In the following steps, you apply a CloudFormation stack to automatically create a new SageMaker AI domain. The stack also creates a user profile and configures the needed environment and permissions. The SageMaker AI domain is configured to let you directly launch Amazon EMR clusters from Studio. For this example, the Amazon EMR clusters are created in the same AWS account as SageMaker AI without authentication. You can find additional CloudFormation stacks supporting various authentication methods like Kerberos in the [getting_started](https://github.com/aws-samples/sagemaker-studio-emr/tree/main/cloudformation/getting_started) GitHub repository. @@ -38 +38 @@ SageMaker AI allows 5 Studio domains per AWS account and AWS Region by default. - 1. Download the raw file of this [AWS CloudFormation template](https://github.com/aws-samples/sagemaker-studio-foundation-models/blob/main/workshop-artifacts/cfn/workshop-cfn.yaml) from the `sagemaker-studio-emr` GitHub repository. + 1. Download the raw file of this [CloudFormation template](https://github.com/aws-samples/sagemaker-studio-foundation-models/blob/main/workshop-artifacts/cfn/workshop-cfn.yaml) from the `sagemaker-studio-emr` GitHub repository. @@ -40 +40 @@ SageMaker AI allows 5 Studio domains per AWS account and AWS Region by default. - 2. Go to the AWS CloudFormation console: [https://console.aws.amazon.com/cloudformation](https://console.aws.amazon.com/cloudformation/) + 2. Go to the CloudFormation console: [https://console.aws.amazon.com/cloudformation](https://console.aws.amazon.com/cloudformation/) @@ -50 +50 @@ SageMaker AI allows 5 Studio domains per AWS account and AWS Region by default. - 3. Upload the downloaded AWS CloudFormation template and choose **Next**. + 3. Upload the downloaded CloudFormation template and choose **Next**. @@ -73 +73 @@ In the following steps, you create a new Amazon EMR cluster from the Studio UI. - 5. On the Amazon EMR clusters page, choose **Create**. Select the template **SageMaker Studio Domain No Auth EMR** created by the AWS CloudFormation stack and then choose **Next**. + 5. On the Amazon EMR clusters page, choose **Create**. Select the template **SageMaker Studio Domain No Auth EMR** created by the CloudFormation stack and then choose **Next**. @@ -163 +163 @@ You are ready to use the `Lab_3_RAG_on_SageMaker_Studio_using_EMR.ipynb` noteboo -## Step 4: Clean up your AWS CloudFormation stack +## Step 4: Clean up your CloudFormation stack @@ -165 +165 @@ You are ready to use the `Lab_3_RAG_on_SageMaker_Studio_using_EMR.ipynb` noteboo -After you are finished, make sure to terminate your two endpoints and delete your AWS CloudFormation stack to prevent continued charges. Deleting the stack cleans up all the resources that were provisioned by the stack. +After you are finished, make sure to terminate your two endpoints and delete your CloudFormation stack to prevent continued charges. Deleting the stack cleans up all the resources that were provisioned by the stack. @@ -167 +167 @@ After you are finished, make sure to terminate your two endpoints and delete you -###### To delete your AWS CloudFormation stack when you are done with it +###### To delete your CloudFormation stack when you are done with it @@ -169 +169 @@ After you are finished, make sure to terminate your two endpoints and delete you - 1. Go to the AWS CloudFormation console: [https://console.aws.amazon.com/cloudformation](https://console.aws.amazon.com/cloudformation/) + 1. Go to the CloudFormation console: [https://console.aws.amazon.com/cloudformation](https://console.aws.amazon.com/cloudformation/) @@ -175 +175 @@ After you are finished, make sure to terminate your two endpoints and delete you -Wait for the stack deletion to complete. This can take a few minutes. AWS CloudFormation automatically cleans up all resources defined in the stack template. +Wait for the stack deletion to complete. This can take a few minutes. CloudFormation automatically cleans up all resources defined in the stack template.