Accuracy metrics like precision and recall provide incomplete pictures of AI model performance in production. Real-world systems must balance multiple competing objectives: accuracy across diverse inputs, robustness to adversarial examples, fairness across demographic groups, and alignment with business goals. Comprehensive evaluation frameworks measure all these dimensions systematically.
Robustness Testing
Production models encounter inputs far more diverse than training data. Robustness testing evaluates model behavior on edge cases, adversarial inputs, and distribution shifts. This includes testing with noisy data, incomplete information, and intentionally challenging examples designed to expose weaknesses. Models that perform well on test sets but poorly on edge cases fail in production.
- Test with deliberately corrupted inputs to measure graceful degradation behavior
- Evaluate performance on out-of-distribution examples that differ from training data
- Measure sensitivity to small input perturbations that shouldn't change predictions
- Assess calibration to ensure confidence scores accurately reflect prediction reliability
- Monitor performance across different subpopulations to detect fairness issues
Business Impact Metrics
Technical metrics must connect to business outcomes. For recommendation systems, this means measuring conversion rates and revenue impact, not just click-through rates. For customer service automation, resolution rates and customer satisfaction matter more than response accuracy. Aligning evaluation with business objectives ensures optimization efforts improve what actually matters.
Continuous Evaluation
Model performance drifts over time as data distributions shift. Continuous evaluation monitors key metrics in production, alerting when performance degrades. Comparing predictions against eventual ground truth enables automated accuracy tracking. User feedback provides qualitative signals about real-world model behavior. This ongoing measurement catches issues before they significantly impact users.