Production Readiness Has a Test Score
Use a production-readiness rubric to distinguish launch blockers from nice-to-have improvements.
A model with a great score can still be production-thin. The Readiness Layers The ML Test Score breaks the release conversation into practical layers: data validation, model validation, infrastructure reliability, and monitoring. Each layer catches a different class of failure. Data tests catch schema, range, freshness, and skew problems. Model tests catch slice regressions, calibration gaps, and stability issues. Infrastructure tests catch serving, versioning, rollback, and dependency failures. Monitoring catches the slow failures that only appear after launch. Why It Works The rubric makes hidden debt visible. Teams often over-weight the metric they just optimized because it is concrete and…
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