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Tool Comparison
VS
At a Glance
| Attribute | DVC | MLflow |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.4/5 | 4.6/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | AI DataMLOps | MLOps |
Key Features
DVC
- Dataset and model versioning with Git-like commands
- Remote storage support: S3, GCS, Azure, SSH, HDFS
- DVC Pipelines for reproducible ML workflows
- Experiment tracking with dvc exp run
- Data registry for sharing datasets across teams
- CI/CD integration for automated ML pipelines
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
DVC
Track and reproduce ML experiments
Initialize DVC in your Git repo: dvc init
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
DVC
mlflowgithub-actionskubeflowwandbairflow
MLflow
kubeflowwandbhuggingfacekubernetesdocker
🏆 Which should you choose?
Choose MLflow if…
- → you're already in the MLOps ecosystem and prefer MLflow's workflow