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MLflow
Open-source platform for tracking machine learning experiments, evaluating models, managing versions and lineage, and preparing models for deployment.
About MLflow
MLflow helps teams organize the work surrounding machine learning models. It records experiments, parameters, metrics, and model artifacts so that results can be inspected and compared rather than remaining scattered across scripts and notebooks.
Its model registry connects model versions with the runs that produced them. Aliases, tags, and descriptions help teams identify candidates and manage their use across development and deployment workflows. Serving tools provide routes for running trained models in local and supported production environments.
MLflow can run locally or through a shared tracking server. Its open-source project is licensed under Apache 2.0. Managed platforms may add hosting, access controls, or governance capabilities with their own commercial terms.
Key Features
- Experiment Tracking: Record parameters, metrics, artifacts, and metadata from training runs.
- Automatic Logging: Capture supported framework information through autologging integrations.
- Model Evaluation: Assess and compare model performance through evaluation workflows.
- Model Registry: Organize registered models and their versions in a shared store.
- Lineage and Aliases: Link models to their originating runs and assign meaningful references to versions.
- Model Serving: Prepare logged models for inference with deployment and environment-management tools.
Pricing
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Open-Source MLflow: Free. Distributed under the Apache 2.0 license. Teams provide or separately purchase the infrastructure used to run it.
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Managed Hosting and Related Services: Provider-specific pricing. Commercial platforms offering managed MLflow have separate terms. Their charges are not a license fee for the open-source project.
Use Cases
- Compare model experiments and their training configurations.
- Record metrics and artifacts automatically during supported training workflows.
- Maintain a versioned model registry with traceable origins.
- Share experiment results through a centralized tracking server.
Pros & Cons
Pros:
- Connects experiment results with model artifacts and versions.
- Supports both manual logging and framework-specific autologging.
- Can be used locally or through shared infrastructure.
Cons:
- Self-hosting requires operating the server and its supporting storage.
- Governance capabilities vary between open-source deployments and managed platforms.
Integrations
scikit-learn, PyTorch, TensorFlow, XGBoost, Keras, Apache Spark, Docker, Amazon SageMaker, Modal
FAQ
What is MLflow used for?
MLflow helps manage machine learning experiments, metrics, artifacts, model versions, evaluation, and deployment workflows.
Is MLflow free?
The open-source project is available under Apache 2.0 without a software license fee. Infrastructure and managed hosting services may have separate costs.
Can MLflow track experiments locally?
Yes. MLflow supports local workflows, while a shared tracking server provides a common endpoint and interface for teams.
What does MLflow autologging capture?
For supported integrations, autologging can record training parameters, metrics, trained models, and associated metadata. The exact information depends on the integration.
What is an MLflow model alias?
An alias is a named reference to a model version. It can identify the version intended for a particular role without embedding a fixed version number in every consumer.
Does MLflow train the model itself?
MLflow works alongside model-training libraries. Its tracking APIs and integrations record the training process and help manage the resulting models.
Does every MLflow deployment include the same governance features?
No. The official registry documentation distinguishes open-source functionality from additional capabilities supplied by managed platforms and their backends.
Last edited
November 6, 2026 at 11:14 AM by Venkatraman Chandrasekaran
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