shadow validation
AssessTechniques
Running candidate systems alongside production traffic without exposing results to users, to compare behavior safely.
Why it's here
Placed in Assess: 2 article(s) of evidence from 2 source(s), led by product launches, with 0 in the last 30 days. Confidence 38%.
Evidence (2)
- 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.
- 8Hugging Face Blog·6/24/2026product_launchFFASR Leaderboard launches for real-world ASR benchmarking
Treble Technologies and Hugging Face have launched the FFASR Leaderboard, an open benchmark for evaluating automatic speech recognition in far-field acoustic conditions. The benchmark uses simulated rooms validated against real measurements and compares model accuracy and speed across realistic noise and reverberation settings.