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.

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.

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.

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.

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.