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MLflow
MLflow is an open-source platform for managing the complete machine learning lifecycle. Track experiments, package models, and serve them with a built-in web UI. Supports experiment tracking, model registry, and project management with SQLite storage.
Deployed0 times
Publisherfuturize.rush
Created2026-04-04
Services
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ToolAI
MLflow
An open-source AI engineering platform for managing the complete machine learning lifecycle. Track experiments, compare runs, package reproducible models, and serve predictions — all from a clean web dashboard.
What You Can Do After Deployment
- Open your domain — access the MLflow Tracking UI to view experiments and runs
- Log experiments — point your ML scripts to this server with
mlflow.set_tracking_uri("https://your-domain") - Compare runs — view metrics, parameters, and artifacts side-by-side across experiments
- Register models — promote experiment runs to the Model Registry for staging and production
- Search and filter — use the built-in search to find runs by metrics, parameters, or tags
Key Features
- Experiment tracking with metrics, parameters, and artifacts
- Model Registry for versioning and lifecycle management
- Web-based dashboard for visualizing and comparing runs
- REST API for programmatic access
- Support for Python, R, Java, and REST clients
- SQLite backend for lightweight self-hosted storage
- Compatible with popular ML frameworks (PyTorch, TensorFlow, scikit-learn, etc.)