Posted On: Apr 14, 2021

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. With a single click, data scientists and ML developers can quickly spin up SageMaker Studio Notebooks for exploring datasets and building models. Now you can use custom resource tags to track and categorize the costs of SageMaker Studio Notebooks by users, groups, lines of businesses, or cost centers.  

A tag is a label that you or AWS assigns to an AWS resource. You can use tags to organize your resources by users, departments or cost centers, and track your AWS costs on a detailed level. The cost allocation tags can be used for categorizing costs in AWS Cost Explorer and AWS Cost and Usage Reports (AWS CUR). In SageMaker Studio, you can assign custom tags to SageMaker Studio domain as well as users who are provisioned access to the domain. Starting today, SageMaker Studio will automatically copy and assign these tags to the SageMaker Studio Notebooks created by the users, so you can easily track and categorize the cost of SageMaker Studio notebooks, and create cost chargeback models for your organization.  

The automated tagging feature is now available in all AWS regions where Amazon SageMaker Studio is available. The SageMaker Studio domain and users can be tagged using AWS CLI, AWS SDK, and AWS CloudFormation templates for SageMaker Studio. To learn more about SageMaker Studio visit the SageMaker user guide.