MLOps.community: AI Evals Must Start at Idea-Time, Not Release-Time
A 126-minute MLOps.community episode featuring eval practitioner Maggie makes the case that AI evaluation is a development discipline, not a release gate. Core thesis: evals must be designed at idea-time, split into distinct pre-production (simulated personas, synthetic scenarios, variance measurement) and post-production (offline analysis plus online behavioral metrics) tracks, and tied to a specific business metric per use case — conversion, satisfaction, or retention. Teams that bolt evals on after shipping "always end up rebuilding their eval system." The episode argues hard against generic 0-1 evaluator scores, outsourced labeling, and 20-evaluator dashboards disconnected from business outcomes. Maggie's team built their own evaluator infrastructure because the market gap is in basic evaluator training, sampling, and failure-mode discovery — not in pushing more dashboards.
Why It Matters
The eval discipline described — business-metric-tied, built-in-house, continuous from idea to post-launch — is the maturity standard that separates production agent systems from demos. With eval tooling still largely immature, this framework gives teams a clear architectural target regardless of which observability vendor they use.