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Kubeflow
Kubernetes-native ML platform — pipelines, training operators, and model serving at scale.
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At a Glance
| Attribute | Kubeflow | MLflow |
|---|---|---|
| License / Pricing | Free | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.2/5 | 4.6/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | MLOps | MLOps |
Key Features
Kubeflow
- Kubeflow Pipelines for reproducible ML workflows
- Training Operators for PyTorch, TensorFlow, XGBoost distributed training
- KServe for standardized model serving with auto-scaling
- Katib for hyperparameter tuning on Kubernetes
- JupyterHub integration for notebook environments
- Multi-user isolation with namespaces
MLflow
- Experiment tracking with metrics, params, and artifacts
- Model Registry for versioning and stage transitions
- MLflow Projects for reproducible ML code
- Serving models via REST API with mlflow models serve
- Auto-logging for sklearn, TensorFlow, PyTorch, XGBoost
- Databricks-hosted MLflow for enterprise use
Real-World Use Cases
Kubeflow
End-to-end ML pipeline on Kubernetes
Define pipeline components as Python functions with @component
MLflow
Track model training experiments
Start an MLflow run with mlflow.start_run()
Register and promote a model to production
Log the trained model with mlflow.sklearn.log_model()
Integrations
Kubeflow
kubernetesmlflowwandbraydocker
MLflow
kubeflowwandbhuggingfacekubernetesdocker
🏆 Which should you choose?
Choose Kubeflow if…
- → you want a managed or commercial offering with enterprise support and SLAs
Choose MLflow if…
- → you need a fully open-source, self-hosted solution with no vendor lock-in