MLflow
MLflow is an open-source platform designed to manage the complete machine learning lifecycle. Originally developed by Databricks, it provides tools for tracking experiments, packaging code, and deploying models.
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Core Components
- MLflow Tracking: Record and query experiments (code, data, config, and results)
- MLflow Projects: Package ML code in a reusable, reproducible form
- MLflow Models: Deploy machine learning models in diverse serving environments
- MLflow Registry: Centralized model store for managing model versions and stages
Key Features
- Experiment Tracking: Log parameters, metrics, and artifacts from ML experiments
- Model Versioning: Track different versions of models with lineage
- Model Packaging: Package models in multiple formats for deployment
- Deployment Support: Deploy to various platforms (cloud, edge, batch)
- Language Agnostic: Works with Python, R, Java, and other languages
- Framework Support: Compatible with TensorFlow, PyTorch, scikit-learn, and more
AI-Assisted Development Features
- Automated Model Evaluation: Compare models across different metrics
- Model Performance Monitoring: Track model drift and performance degradation
- Reproducible Experiments: Ensure consistent results across different environments
- Collaborative ML Development: Share experiments and models across teams
Benefits
- Reduces time from experimentation to production
- Improves model governance and compliance
- Enables collaborative ML development
- Provides audit trails for ML projects