MLflow
by Databricks · open-source · Last verified 2026-03-21
MLflow is an open-source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. It provides tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models.
https://mlflow.org/ ↗B+
B+—Good
Adoption: AQuality: AFreshness: A+Citations: B+Engagement: B+
Specifications
- License
- Apache License 2.0
- Pricing
- open-source
- Capabilities
- experiment tracking, model management, model deployment, reproducible runs
- Integrations
- Spark, Kubernetes, AWS Sagemaker, Azure ML, GCP Vertex AI
- Use Cases
- managing machine learning projects, tracking model performance, deploying models to production, collaborating on ML projects
- API Available
- Yes
- Tags
- machine-learning, experiment-tracking, model-registry, deployment, reproducibility
- Added
- 2026-03-21
- Completeness
- 100%
Index Score
79.2Adoption
85
Quality
80
Freshness
90
Citations
75
Engagement
70
Put AI to work for your business
Deploy this framework alongside autonomous AaaS agents that handle tasks end-to-end — no babysitting required.