DevOps✓ Mathematical
◆ The PatternAutomated testing of data, models, and deployments
ML CI/CD extends traditional CI/CD with three additional test layers: data validation (schema + quality), model validation (performance thresholds), and serving validation (latency + correctness). A merge should trigger retraining, evaluation, and conditional deployment.
// Interactive — CI/CD pipeline stages
| Stage | Tests | Gate |
|---|---|---|
| Data | Schema, freshness, completeness | All checks pass |
| Training | Convergence, no NaN loss | Loss below threshold |
| Evaluation | AUC, F1, latency benchmark | Metrics ≥ champion |
| Deployment | Smoke test, shadow run | No errors in canary |
Pattern bridge: CI/CD gates are automated significance tests — the model must prove it's better before shipping. The same "don't trust your intuition, trust the numbers" principle that cognitive bias awareness teaches.