ML Model Documentation and Evaluation
Model cards, evaluation methodology, bias testing frameworks, and benchmark references. Build transparent, reproducible, and responsible AI documentation.
Model Cards
Understand the origin, structure, and purpose of model cards as standardized documentation for ML models.
Evaluation Guides
Learn model evaluation methodology, fairness testing, and adversarial red teaming techniques.
References
Browse ML benchmark datasets, leading scores, and evaluation framework comparisons.
Key Topics
Transparency
Standardized model documentation that communicates intended use, limitations, and performance characteristics.
Evaluation
Rigorous methodology for train/val/test splits, cross-validation, and metric selection by task type.
Fairness
Bias detection across demographic groups using demographic parity, equalized odds, and calibration metrics.
Safety
Red teaming techniques including prompt injection testing, jailbreak categorization, and adversarial robustness.