“- Central registry for both models and datasets.”
“- Versioning, aliases, lineage, metadata, and access control.”
“- Teams can discover and reuse published artifacts across projects.”
“- Integrates with ML training and experiment workflows.”
“W&B Registry | Yes | Yes | Deployment options vary | Unified ML registry”
“- Hosts models, datasets, and other ML assets.”
“- Git-based repositories with version history and revisions.”
“- Private organizational repositories and role-based access.”
“- Python and ML framework integrations.”
“Hugging Face Hub | Yes | Yes | Enterprise options vary | Model and dataset sharing”
“- Centralized model registration and versioning.”
“- Model aliases, metadata, lineage, and annotations.”
“- REST APIs and Python SDK.”
“- Can use S3, Azure Blob, or other artifact storage backends.”
“MLflow | Yes | Via artifacts/integrations | Yes | Open-source MLOps”
“Important: MLflow's Model Registry is primarily model-centric. For datasets, you would typically combine it with MLflow artifacts, DVC, or another data versioning solution.”
“- Centralized governance of models, data, and other assets.”
“- Fine-grained permissions and cross-workspace sharing.”
“- Data and model lineage.”
“- Integrates with MLflow.”
“Databricks UC | Yes | Yes | Managed platform | Enterprise governance”
“- Model discovery through a catalog.”
“- Central index of models, versions, and artifact metadata.”
“- Integrates with the wider Kubeflow ecosystem and KServe.”
“Kubeflow | Yes | Limited | Yes | Kubernetes ecosystem”
“Important: Its registry focuses on models rather than being a complete dataset repository.”