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Tool Comparison
Ray
Scale Python AI workloads from laptop to cluster — distributed training and serving.
VS
At a Glance
| Attribute | Ray | MLflow |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.5/5 | 4.6/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | MLOps | MLOps |
Key Features
Ray
- Ray Core — distributed task and actor execution
- Ray Train — distributed ML training with PyTorch, TensorFlow
- Ray Serve — scalable model serving with FastAPI integration
- Ray Tune — distributed hyperparameter tuning
- KubeRay operator for Kubernetes deployment
- Anyscale cloud for managed Ray clusters
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
Ray
Distributed fine-tuning of a large model
Wrap your training function with @ray.remote
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
Ray
mlflowwandbkubeflowkuberneteshuggingface
MLflow
kubeflowwandbhuggingfacekubernetesdocker
🏆 Which should you choose?
Choose MLflow if…
- → you're already in the MLOps ecosystem and prefer MLflow's workflow