Can you recommend a model registry for tracking experiments and reproducible training runs in an enterprise ML team?

Yes — for an enterprise ML team, I’d recommend MLflow Model Registry as the default choice in many cases.

Why MLflow is a strong fit

• Experiment tracking + model registry in one place
• Reproducible runs: log params, metrics, artifacts, code version, and environment
• Lifecycle management: stage models through development → staging → production
• Framework-agnostic: works with scikit-learn, XGBoost, PyTorch, TensorFlow, etc.
• Enterprise-friendly deployment options
  • Self-hosted
  • Can integrate with existing storage, auth, and CI/CD
• Open source and widely adopted, which helps with long-term portability

When to choose something else

• If your team is already deeply in the Databricks ecosystem: use Databricks Unity Catalog / Model Registry
• If you want a fully managed cloud ML platform:
  • AWS SageMaker Model Registry
  • Google Vertex AI Model Registry
  • Azure Machine Learning Registry
• If you need strong governance and feature store integration: platform-native registries can be a better fit

Enterprise checklist for your registry

Look for:

• Role-based access control
• Approval workflows
• Audit logs
• Artifact/version lineage
• CI/CD integration
• Support for metadata like data snapshot, code commit, and training environment
• Model promotion across environments
• API access for automation

Practical recommendation

If you’re building a platform-neutral enterprise ML workflow, start with:

MLflow + a secure artifact store + CI/CD + metadata logging

That gives you reproducible training runs and a usable registry without locking you into one cloud.

If you want, I can also compare MLflow vs SageMaker vs Vertex AI vs Azure ML for enterprise model registry use cases.