A/B Testing Patterns for Machine Learning Models
Designing and running A/B tests for ML model changes. Traffic splitting, metric selection, statistical rigor, and common pitfalls.
Model-Evaluation
Comprehensive Model Evaluation Beyond Accuracy
How to evaluate ML models holistically, covering performance metrics, fairness analysis, robustness testing, and business impact assessment.
Confusion Matrix
What a confusion matrix is, how to read it, and how it connects to precision, recall, and other classification metrics.
Cross-Validation
What cross-validation is, how it provides robust model performance estimates, and when to use different cross-validation strategies.
F1 Score
What the F1 score measures, when to use it as a model evaluation metric, and its limitations.
Precision and Recall
What precision and recall measure, how to choose between them, and why the tradeoff matters for business-critical AI systems.
ROC Curve
What ROC curves and AUC measure, how to interpret them, and when to use ROC versus precision-recall analysis.
Shadow Deployment Pattern for AI Models
Running new AI models in parallel with production models to compare outputs without affecting users. Implementation, comparison strategies, …