Kubeflow Hub (Model Registry)
KubeflowKubeflow component, formerly called Model Registry, where teams register models, model versions and their artifacts through a REST API and Python client and track them from experimentation to production. The registry stores metadata and the URI of each artifact in S3 or an OCI registry, and its client can upload a local model and register it in one call. A companion catalog lets users browse models from external sources such as Hugging Face before registering them. The component is still marked alpha.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public(github.com/kubeflow/hub)
- core features withheld
- no — a component of Kubeflow, a CNCF graduated project
The registry and catalog are Apache-2.0 and install standalone or with Kubeflow. Red Hat drives its development, but the repository carries no enterprise code or license check.
- https://raw.githubusercontent.com/kubeflow/hub/HEAD/LICENSE recorded 2026-09-27
Apache License Version 2.0, January 2004, full text.
- https://raw.githubusercontent.com/kubeflow/hub/HEAD/README.md recorded 2026-09-27
"Kubeflow Hub is an umbrella project including the Model Registry ... and the Catalog"; "Red Hat drives the project's development through Open Source principles, ensuring transparency, sustainability, and community ownership."
- https://www.kubeflow.org/docs/components/model-registry/overview/ recorded 2026-09-27
"Kubeflow is now a CNCF graduated project"; "© 2026 The Kubeflow Authors."
Adoption
not assessedKubeflow Hub runs inside each organization's own cluster, and no install or user count is published. Downloads of its Python client would measure a client rather than the registry, so no reading was recorded.
- https://raw.githubusercontent.com/kubeflow/hub/HEAD/README.md recorded 2026-09-27
"This Kubeflow component has alpha status with limited support." No usage or install figure in the README.
Capability
4 medium confidenceTeam members register models, versions and artifacts through a documented API, and the client uploads the files to the deployment's own object store or OCI registry as it registers them. It tracks models only and keeps weights in the storage it points to rather than serving them itself, which keeps it a step below Hugging Face Hub.
- https://model-registry.readthedocs.io/en/latest/ recorded 2026-09-27
"To both upload and register a model, use the convenience method upload_artifact_and_register_model. This method supports both s3-based storage (via boto3) as well as OCI-based image registries"
- https://raw.githubusercontent.com/kubeflow/hub/HEAD/docs/logical_model.md recorded 2026-09-27
RegisteredModel "Represent logically a given ML model", ModelVersion "a specific version", ModelArtifact "the ML model artifact"; "`uri` can be used as a general mechanism to locate the model artifact/file on storage."
- https://www.kubeflow.org/docs/components/model-registry/overview/ recorded 2026-09-27
"a metadata store where teams register, version, and track their own models"; the catalog "does not store model weights or artifacts".
Verified 2026-09-27