Monitoring for drift and regressions

This is Lesson 5.5, Launch Readiness. Here is the situation. The switchback experiment from Lesson 5.4 delivered clear results. The v2 change improved SQL success rate by 4.8 percentage points, the valid estimate after correcting for cache interference. The confidence interval excludes zero: [+2.1, +7.5], p=0.003. The PM reads the one-pager and says "ship it." And then three questions from engineering stop the conversation cold. Is the monitoring infrastructure ready to detect silent failures after launch? What happens if quality degrades 5% overnight; is there a runbook? Have the offline metrics been validated to predict what users experience in production? None of these can be answered with the evidence available so far. This is the gap that kills AI launches. Not bad models, but bad operational readiness. Teams spend weeks running experiments and building evaluation pipelines, then ship without monitoring, without rollback plans, without validating that what they measured offline matches what users actually experience. This lesson closes that gap using a staged rollout framework. It covers how to move from "we ran an experiment" to "we're ready to ship" through four progressive stages: shadow, canary, ring 1, and full rollout, each with explicit entry criteria and exit criteria. By the end, the deliverable is a Rollout Decision Document that synthesizes technical validation, operational readiness, and decision governance into a single artifact suitable for a launch review. This is the last piece of the evaluation puzzle before moving to production monitoring.