MLflow for Traditional Machine Learning
MLflow provides comprehensive experiment tracking, model management, and deployment capabilities for traditional machine learning workflows. From scikit-learn pipelines to gradient boosting models, MLflow streamlines your path from experimentation to production.
Get Started
Scikit-learn Guide
Start with scikit-learn autologging, model management, and deployment patterns.
XGBoost Guide
Learn gradient boosting with automatic parameter and feature importance tracking.
Spark MLlib Guide
Scale traditional ML to big data with distributed computing.
Hyperparameter Tuning
Optimize models with GridSearchCV, RandomizedSearchCV, and Optuna integration.
Model Evaluation
Evaluate models with built-in metrics, visualizations, and custom evaluators.
Model Deployment
Deploy models to production with MLflow serving and cloud platforms.
Why MLflow for Traditional ML?
Automatic Logging
Single line of code (mlflow.autolog()) captures parameters, metrics, models, and artifacts for scikit-learn, XGBoost, LightGBM, and more.
Experiment Organization
Track hyperparameter searches with parent-child runs. Compare models across algorithms with visual charts and sortable tables.
Pipeline Tracking
Automatically log scikit-learn Pipeline components, preprocessing steps, and feature transformations with full reproducibility.
Flexible Deployment
Deploy models for real-time inference, batch processing, or edge deployment with Docker, Kubernetes, and cloud platform support.
Supported Libraries
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Model Registry
Manage model versions, aliases, and deployment lifecycle with centralized governance.
MLflow Tracking
Track experiments, parameters, metrics, and artifacts across all ML workflows.
Custom PyFunc Models
Create standardized, reproducible model interfaces with MLflow's PyFunc framework.