drift detection
AssessTechniques
Methods for detecting gradual changes in model behavior, data, or retrieval quality over time.
Why it's here
Placed in Assess: 1 article(s) of evidence from 1 source(s), led by framework updates, with 0 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 6The New Stack·7/2/2026framework_updateWhy CI/CD Gates Fail for LLM Pipelines
The article argues that traditional CI/CD gates are insufficient for production LLM systems because model behavior is probabilistic and can drift without triggering hard failures. It proposes release gates based on baseline evaluations, drift detection, shadow validation, and cost/latency guardrails to catch silent regressions before users see them.