Why Accuracy Isn't Enough: Comprehensive ML Model Evaluation for Production Systems

Why Accuracy Isn’t Enough: Comprehensive ML Model Evaluation for Production Systems Building ML models that work in production requires fundamentally different evaluation approaches than academic exercises. Over the past three years architecting real-time sports prediction systems and customer intelligence platforms at a leading Australian sports technology company, I’ve learned that a model showing 95% accuracy in testing can still fail catastrophically in production. The difference between research and production ML isn’t just scale—it’s understanding that evaluation is where theory meets business reality.