Building a Production ML Prediction Engine with Bayesian Evaluation and CLV Tracking
The Situation
A predictive decision engine project required a robust, production-grade ML system capable of generating probabilistic outputs against real-world outcomes, held to strict commercial performance standards.
The system needed to move beyond simple model accuracy metrics to demonstrate genuine predictive edge measured in financial terms.
The Task
Design and build the full ML pipeline from scratch — data architecture, feature engineering, model training, inference, and continuous optimisation — with a rigorous evaluation framework to validate commercial viability.
The Action / Approach
Built a multi-layer ML architecture processing 2.4M+ strength ratings across 100,000+ contests.
Implemented a 20-feature Random Forest model, evaluated against Bayesian alternatives through structured comparative testing.
Designed a CLV-based commercial evaluation framework to measure real-world predictive edge rather than model accuracy alone.
Established a layered gate system ensuring each component met defined performance thresholds before progression.
The Result
Production system delivering live probabilistic predictions with accumulated CLV tracking confirming positive predictive edge.
Bayesian evaluation concluded RF model retained over alternatives — demonstrating that architectural discipline, not model complexity, drives commercial performance.
