Amazon SageMaker Collections is a new capability to organize models in the Model Registry
Amazon SageMaker announced Collections, a new capability to organize your machine learning models in the Amazon SageMaker Model Registry. You can use Collections to group registered models that are related to each other and organize them in hierarchies to improve model discoverability at scale.
Amazon SageMaker announced Collections, a new capability to organize your machine learning models in the Amazon SageMaker Model Registry. You can use Collections to group registered models that are related to each other and organize them in hierarchies to improve model discoverability at scale.
Amazon SageMaker Model Registry is a purpose-built tool for machine learning operations (MLOps) to help you centrally manage your ML models. You can track models and metadata, compare model versions, and review and approve them for deployment through the Amazon SageMaker Model Registry. When you register a model, Amazon SageMaker Model Registry creates a Model Package and stores all successive versions of the model under one Model Package Group.
With Collections you can organize registered models that are associated with one another. For example, you could categorize your models based on the domain of the problem they solve under Collections titled ‘NLP-models, ’CV-models’, and ‘Speech-recognition-models’. To organize your registered models in a tree structure you can nest Collections within each other. Any operations you perform on a Collection (create/read/update/delete) will not alter your registered models. You can use the Amazon SageMaker Studio UI or the Python SDK to manage Collections.
Amazon SageMaker Model Registry is available in all AWS Regions, except the AWS GovCloud (US) Regions and China Regions.
To get started, create your first Collection for registered models via the Amazon SageMaker Studio UI or via the Amazon SageMaker Python SDK. Visit the Amazon SageMaker developer guide for additional information on Collections.