Overview
RankForge fills the critical gap between Learning-to-Rank (LTR) model libraries like XGBoost and LightGBM and production deployment. It provides a comprehensive orchestration layer with pluggable feature stores, replay-based backtesting, A/B test harness, and FastAPI serving with a consistent, model-agnostic interface.
Key Features
- Model-Agnostic Interface: Works seamlessly with XGBoost, LightGBM, and other LTR libraries
- Pluggable Feature Stores: Flexible integration with various feature store backends
- Replay-Based Backtesting: Simulate historical scenarios for model validation
- A/B Testing Framework: Built-in harness for production A/B testing
- FastAPI Serving: High-performance REST API for model serving
- Production-Ready: Designed for enterprise-scale deployments
Technical Implementation
Core Architecture
- Feature Store Integration: Pluggable backends for feature retrieval
- Model Pipeline: Unified interface for model training and inference
- Backtesting Engine: Replay-based evaluation on historical data
- A/B Test Harness: Statistical testing and variant management
- API Server: FastAPI-based serving with monitoring
Ranking Pipeline
- Feature engineering and transformation
- Model training and validation
- Ranking and scoring
- Result aggregation and serving
Key Capabilities
- End-to-end LTR pipeline management
- Historical data replay for backtesting
- Statistical significance testing
- Real-time serving with low latency
- Comprehensive logging and monitoring
- Easy model versioning and rollback
Code Repository
Explore the implementation on GitHub:
git clone https://github.com/Kernel-ML/rankforge.git
cd rankforge
pip install -e .
rankforge serve --config config.yaml
Use Cases
- E-commerce search ranking
- Recommendation system ranking
- Information retrieval ranking
- Personalized ranking pipelines
- Multi-objective ranking optimization
Future Enhancements
- Support for neural ranking models
- Advanced feature engineering tools
- Real-time model updates
- Distributed serving capabilities
- Enhanced monitoring and observability
Technologies Used
PythonFastAPIXGBoostLightGBM