ML Pipeline for Demand Forecasting
End-to-end ML pipeline with conformal prediction for a major fashion retailer
1Problem
A leading global fast fashion retailer needed accurate demand forecasts with uncertainty quantification. Traditional point forecasts led to either overstock (waste) or stockouts (lost sales). The existing system couldn't provide reliable confidence intervals and lacked production-grade infrastructure.
2Solution
Built an end-to-end ML pipeline with conformal prediction for distribution-free prediction intervals. The system includes automated feature engineering, model training with cross-validation, and a serving layer that outputs both point forecasts and calibrated uncertainty bounds. Integrated with existing inventory systems via REST API.
3Technical Details
Production ML pipeline for demand forecasting at scale. Features conformal prediction for statistically rigorous uncertainty quantification, automated retraining, model versioning, A/B testing framework for model comparison, and real-time monitoring dashboards. Built to handle the complexity of global fashion retail with seasonal patterns and trend shifts.
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