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.
Statistics
PCA - Principal Component Analysis
What PCA is, how it identifies principal components, and when to use it for dimensionality reduction in ML pipelines.
Statistical Assertion Pattern
A testing pattern for non-deterministic AI outputs: run N times, assert success rate exceeds threshold, use confidence intervals to account …
Testing Non-Deterministic Systems
Strategies for testing AI systems where the same input produces different outputs: statistical assertions, distribution testing, confidence …