ML Testing and the ML Test Score

Traditional software tests check deterministic behavior. ML systems are probabilistic, so you need broader coverage: data, training, serving, and monitoring.

What “production-ready” means

• data is validated
• training is reproducible
• serving matches training assumptions
• monitoring detects silent failures
• rollback is possible

A pragmatic test checklist

Data tests

• schema checks
• missingness/coverage
• distribution sanity

Training tests

• deterministic seeds where possible
• training data versioning
• metric regression checks

Serving tests

• training/serving skew checks
• latency budget checks

Monitoring

• drift detection
• alerting for stale features
• outcome monitoring and feedback loops

Why interviewers like this

It shows you understand ML technical debt and can build systems that don’t degrade silently.