Champion Challenger Framework
Automated ML pipeline conducting monthly training cycles to challenge champion models, enabling continuous optimization and deployment at Poland's largest bank.
Business Challenge
At PKO BP, machine learning models power critical business decisions affecting millions of customers. However, model performance naturally degrades over time due to data drift, changing customer behaviors, and market conditions.
The bank needed an automated system to continuously evaluate and improve their ML models without manual intervention, ensuring optimal performance while minimizing operational risk.
Solution Architecture
I designed and implemented a comprehensive Champion-Challenger framework that automates the entire model lifecycle:
- Automated Data Pipeline: Monthly data ingestion from multiple sources with quality validation and feature engineering
- Multi-Model Training: Parallel training of multiple challenger models using different algorithms and hyperparameters
- Rigorous Evaluation: Statistical testing framework comparing challenger models against the current champion
- Automated Deployment: Safe model promotion with rollback capabilities and monitoring alerts
Technical Implementation
The framework was built using enterprise-grade technologies to handle PKO BP's scale and reliability requirements:
- Apache Airflow: Orchestrated complex workflows with dependency management and error handling
- MLflow: Model registry and experiment tracking for version control and reproducibility
- PySpark: Distributed processing of massive datasets (100M+ records) with optimal resource utilization
- Statistical Testing: Implemented A/B testing framework with proper significance testing and power analysis
- Monitoring & Alerting: Real-time model performance monitoring with automatic alerts for degradation
Business Impact
The Champion-Challenger framework delivered significant value across multiple dimensions:
- 15% improvement in average model performance across all use cases
- 90% reduction in manual model maintenance effort
- Zero downtime deployments with automatic rollback
- Risk reduction through systematic testing and validation
- Faster time-to-market for new model improvements
Key Features
- Automated Model Selection: Algorithm automatically selects best performing challenger based on multiple metrics
- Gradual Rollout: Safe deployment with gradual traffic increase and performance monitoring
- Compliance Ready: Full audit trails and documentation for regulatory requirements
- Resource Optimization: Intelligent resource allocation and cost optimization across training jobs