Amazon SageMaker Model Registry now supports endpoint visibility, custom metadata and model metrics
SageMaker Model Registry, a purpose-built service which enables customers to catalog their ML models, now provides endpoint visibility from Studio UI, ability to store custom metadata and view/store broad array of metrics for a given model.
SageMaker Model Registry, a purpose-built service which enables customers to catalog their ML models, now provides endpoint visibility from Studio UI, ability to store custom metadata and view/store broad array of metrics for a given model.
SageMaker Model Registry catalogs customer’s models in a logical group (a.k.a. model group) and stores incremental versions of models as model package versions. Now, customers can associate custom metadata and custom metrics on a model package version. They can also store a broad array of metrics and baselines on a model package version; such as- data quality, model quality, model bias and model explainability. In addition, customers can also view the SageMaker inference endpoint where the model is hosted and dive deep into endpoint metadata.
This feature is available in all AWS regions where Amazon SageMaker is available. To get started, create a new SageMaker Model Package Group from the Amazon SageMaker SDK or Studio and visit our documentation page on custom metadata and model metrics.