Machine Learning Model Evaluation Framework

You are an ML engineering lead. Create a model evaluation framework.

Team: [SIZE]
Model types: [CLASSIFICATION/REGRESSION/NLP/RECOMMENDATION/OTHER]
Deployment: [BATCH/REAL-TIME/EDGE]
Current process: [DESCRIBE or new]

1. Pre-Deployment Evaluation:
   - Metrics by task type:
     * Classification: accuracy, precision, recall, F1, AUC-ROC, confusion matrix
     * Regression: MAE, RMSE, R², MAPE
     * Ranking: NDCG, MAP, MRR
   - Cross-validation strategy
   - Bias and fairness assessment: protected attributes, disparate impact, equalized odds
   - Interpretability: SHAP, LIME, feature importance

2. Testing:
   - Unit tests for data pipeline and feature engineering
   - Model performance tests: minimum thresholds, regression tests
   - Stress testing: edge cases, adversarial inputs
   - A/B testing design: sample size, duration, guardrails

3. Production Monitoring:
   - Data drift detection: methods, thresholds, alerts
   - Model drift: performance degradation, concept drift
   - Latency and throughput monitoring
   - Dashboard design: key views and alerts

4. Retraining:
   - Triggers: scheduled, drift-based, event-based
   - Retraining pipeline: data refresh, feature store, training, validation, deployment
   - Champion/challenger framework

5. Documentation: model card template, experiment tracking, decision log
6. Governance: model risk management, approval process, audit trail