Core registry
A core registry is a template for specific use cases: Models and Datasets. By default, the Models registry is configured to accept"model"
artifact types and the Dataset registry is configured to accept "dataset"
artifact types. An admin can add additional accepted artifact types.

Custom registry
Custom registries are not restricted to"model"
artifact types or "dataset"
artifact types.
You can create a custom registry for each step in your machine learning pipeline, from initial data collection to final model deployment.
For example, you might create a registry called “Benchmark_Datasets” for organizing curated datasets to evaluate the performance of trained models. Within this registry, you might have a collection called “User_Query_Insurance_Answer_Test_Data” that contains a set of user questions and corresponding expert-validated answers that the model has never seen during training.

Summary
The proceeding table summarizes the differences between core and custom registries:Core | Custom | |
---|---|---|
Visibility | Organizational visibility only. Visibility can not be altered. | Either organization or restricted. Visibility can be altered from organization to restricted visibility. |
Metadata | Preconfigured and not editable by users. | Users can edit. |
Artifact types | Preconfigured and accepted artifact types cannot be removed. Users can add additional accepted artifact types. | Admin can define accepted types. |
Customization | Can add additional types to the existing list. | Edit registry name, description, visibility, and accepted artifact types. |