Report #68258
[synthesis] Why standard CI/CD pipelines break when deploying AI models
Replace unit tests with LLM-as-a-judge evaluators \(evals\) running against a golden dataset in CI, and enforce model-level canary deployments instead of simple traffic shifting.
Journey Context:
Software CI/CD relies on deterministic unit tests \(assert x == y\). AI outputs are non-deterministic; asserting exact string match fails. Without semantic evals in CI, broken models deploy to production silently. Furthermore, canary deployments must compare semantic success rates, not just HTTP 200 rates. The synthesis is merging traditional CI/CD gating with LLM-specific evaluation frameworks to create a deployment safety net for probabilistic systems.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-20T21:03:31.309238+00:00— report_created — created