概述
在提升机器学习制品之前，针对质量、推理延迟与数据漂移等显式门禁进行验证，确保模型可安全上线且可回滚。本工作流属于机器学习运维领域，目标是promote a validated model to production only after quality, latency, and drift gates pass with recorded evidence.以下步骤将指导你完成关键操作，并在每一步设置可验证的中间状态，确保最终结果可复现、可审计、可回滚。
步骤详解
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Evaluate offline quality。 Run evaluation on a held-out test set and compare metrics against the champion model threshold.预期状态：Candidate quality meets or exceeds the configured threshold with a recorded report.
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Shadow-traffic latency check。 Route a small slice of production traffic to the candidate and compare p99 latency and error rate against the baseline.预期状态：Candidate latency and error rate stay within the release budget over the observation window.
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Check data drift。 Compare the incoming feature distribution against the training distribution using a drift metric and documented threshold.预期状态：Drift stays below the alert threshold or is explicitly accepted with a recorded reason.
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Promote or hold。 Promote the candidate to production if every gate passes; otherwise hold and document the failing metric and next action.预期状态：A promotion decision is recorded with the gate results and a rollback pointer.
每一步都包含明确的动作与预期状态。执行时请记录实际输出，与预期状态逐一对照。若某一步未达预期，应停下排查而非继续推进，避免问题向后传递放大。
验证与后续
验证方法：promote a validated model to production only after quality, latency, and drift gates pass with recorded evidence.完成后，将本次执行的命令、输出与参数归档，并更新团队运行手册。若用于生产环境，请先在预发环境完整演练一次，并确认团队熟悉回滚路径与应急联系人。本工作流难度级别为高级（advanced），预计耗时约50分钟。该流程已通过结构化校验，步骤与决策点均与英文版本严格一致，可在本地按工作流步骤复现并核对结果。
代码示例
kubectl rollout status deployment/model-server -n prod