Amazon SageMaker Now Supports Tagging for Hyperparameter Tuning Jobs
Amazon SageMaker now supports tagging for hyperparameter tuning jobs. With this new capability, customers can now add one or more tags to a tuning job that is launched with Automatic Model Tuning.
Amazon SageMaker now supports tagging for hyperparameter tuning jobs. With this new capability, customers can now add one or more tags to a tuning job that is launched with Automatic Model Tuning.
The Automatic Model Tuning (AMT) feature within Amazon SageMaker enables customers to automatically find the most accurate machine learning model through a process called hyperparameter optimization. AMT launches multiple training jobs within a single parent job to discover the ideal weighting of model parameters. Tags can now be added to the parent tuning job and these tags are then propagated to the individual training jobs underneath. Customers can use these tags for purposes such as cost allocation or access control. Previously, customers needed to add individual tags to each of the training jobs underneath rather than the parent tuning job to meet these purposes.
Tagging for hyperparameter tuning jobs is now available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Tokyo), Asia Pacific (Seoul) and Asia Pacific (Sydney) AWS Regions. For additional information, please visit the documentation here.