Track experiments and manage your ML development
MLflow Tracking provides comprehensive experiment logging, parameter tracking, metrics visualization, and artifact management.
Key Benefits:
Experiment Organization: Track and compare multiple model experiments
Metric Visualization: Built-in plots and charts for model performance
Artifact Storage: Store models, plots, and other files with each run
Collaboration: Share experiments and results across teams
Manage model versions and lifecycle
Core Features
MLflow Model Registry provides centralized model versioning, stage management, and model lineage tracking.
Key Benefits:
Version Control: Track model versions with automatic lineage
Stage Management: Promote models through staging, production, and archived stages
Collaboration: Team-based model review and approval workflows
Model Discovery: Search and discover models across your organization
Deploy models to production environments
Core Features
MLflow Deployment supports multiple deployment targets including REST APIs, cloud platforms, and edge devices.
Key Benefits:
Multiple Targets: Deploy to local servers, cloud platforms, or containerized - enronments
Model Serving: Built-in REST API serving with automatic input validation
Batch Inference: Support for batch scoring and offline predictions
Production Ready: Scalable deployment options for enterprise use
Explore Native MLflow ML Library Integrations
Integrates with
Scikit-learn
XGBoost
TensorFlow
PyTorch
Keras
Spark MLlib
Evaluate and validate your ML models
Core Features
MLflow Evaluation provides comprehensive model validation tools, automated metrics calculation, and model comparison capabilities.
Key Benefits:
Automated Metrics: Built-in evaluation metrics for classification, regression, and - mo
Custom Evaluators: Create custom evaluation functions for domain-specific metrics
Model Comparison: Compare multiple models and versions side-by-side
Validation Datasets: Track evaluation datasets and ensure reproducible results
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