How do you programmatically promote a retrained model to production?
WHAT IT TESTS: Gated promotion balancing statistics and safety. ANSWER OUTLINE: Compare on held-out data using significant metric uplift, schema, latency, and drift checks before shadow release. RED FLAG: Using training accuracy without variance checks.
WHAT IT TESTS: Designing a gated promotion process that balances offline statistical rigor with production safety. ANSWER OUTLINE: Use champion-challenger on a held-out test set; require the challenger to beat the incumbent on a primary metric like AUC-ROC by a statistically significant margin with a minimum effect size. Add guardrails for schema validation, latency SLOs, and data drift. Run shadow or canary before full promotion with automatic rollback. RED FLAG: Promoting on training loss without confidence intervals or gradual rollout.
Read the original → docs.cloud.google.com
- #mlops
- #model-deployment
- #ci-cd
- #champion-challenger
- #canary
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