AWS sagemaker documentation change
Summary
Updated provisioning options to include S3 bucket template storage and recommended S3 over Service Catalog for template management
Security assessment
The change emphasizes using S3 buckets with version control for template storage, which improves security practices through versioning and regional control. However, there's no evidence of addressing a specific security vulnerability.
Diff
diff --git a/sagemaker/latest/dg/sagemaker-projects-whatis.md b/sagemaker/latest/dg/sagemaker-projects-whatis.md index e64f01ba6..5c104c760 100644 --- a//sagemaker/latest/dg/sagemaker-projects-whatis.md +++ b//sagemaker/latest/dg/sagemaker-projects-whatis.md @@ -11 +11 @@ SageMaker Projects help organizations set up and standardize developer environme -You can provision SageMaker Projects from the AWS Service Catalog using custom or SageMaker AI-provided templates. For information about the AWS Service Catalog, see [What Is AWS Service Catalog](https://docs.aws.amazon.com/servicecatalog/latest/dg/what-is-service-catalog.html). With SageMaker Projects, MLOps engineers and organization admins can define their own templates or use SageMaker AI-provided templates. The SageMaker AI-provided templates bootstrap the ML workflow with source version control, automated ML pipelines, and a set of code to quickly start iterating over ML use cases. +You can provision SageMaker Projects using custom templates that are stored in Amazon S3 buckets, or by using templates from the AWS Service Catalog or SageMaker AI. For information about the AWS Service Catalog, see [What Is AWS Service Catalog](https://docs.aws.amazon.com/servicecatalog/latest/dg/what-is-service-catalog.html). With SageMaker Projects, MLOps engineers and organization admins can define their own templates or use SageMaker AI-provided templates. The SageMaker AI-provided templates bootstrap the ML workflow with source version control, automated ML pipelines, and a set of code to quickly start iterating over ML use cases. @@ -23 +23,7 @@ Every organization has its own set of standards and practices that provide secur -Organizations often need tight control over the MLOps resources that they provision and manage. Such responsibility assumes certain tasks, including configuring IAM roles and policies, enforcing resource tags, enforcing encryption, and decoupling resources across multiple accounts. SageMaker Projects can support all these tasks through custom template offerings where organizations use AWS CloudFormation templates to define the resources needed for an ML workflow. Data Scientists can choose a template to bootstrap and pre-configure their ML workflow. These custom templates are created as Service Catalog products and you can provision them in the Studio or Studio Classic UI under **Organization Templates**. The Service Catalog is a service that helps organizations create and manage catalogs of products that are approved for use on AWS. For more information about creating custom templates, see [Build Custom SageMaker AI Project Templates – Best Practices](https://aws.amazon.com/blogs/machine-learning/build-custom-sagemaker-project-templates-best-practices/). +Organizations often need tight control over the MLOps resources that they provision and manage. Such responsibility assumes certain tasks, including configuring IAM roles and policies, enforcing resource tags, enforcing encryption, and decoupling resources across multiple accounts. SageMaker Projects can support all these tasks through custom template offerings where organizations use AWS CloudFormation templates to define the resources needed for an ML workflow. Data Scientists can choose a template to bootstrap and pre-configure their ML workflow. + +To get started, we recommend that you create and store custom templates inside an Amazon S3 bucket. Doing so lets you create a bucket in any supported Region for your organization. S3 supports version control, so you can maintain multiple versions of your templates and roll back if necessary. For information about how to create a project from template store in an Amazon S3 bucket, see [Using a template from an Amazon S3 bucket](./sagemaker-projects-templates-custom.html#sagemaker-projects-templates-s3). + +Alternatively, you can also create custom templates as Service Catalog products and you can provision them in the Studio or Studio Classic UI under **Organization Templates**. The Service Catalog is a service that helps organizations create and manage catalogs of products that are approved for use on AWS. For more information about creating custom templates, see [Build Custom SageMaker AI Project Templates – Best Practices](https://aws.amazon.com/blogs/machine-learning/build-custom-sagemaker-project-templates-best-practices/). + +While you can use either option, we recommend that you use S3 buckets over the Service Catalog, so you can create a bucket in supported Regions where SageMaker AI is available without needing to manage the complexities of the Service Catalog.