Explore all 5 guides related to AI Deployment & Release Workflow.
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"Bump the tag and restart — ask Priya if it breaks" is not a runbook, and Priya is on a plane. Here's how to turn what you actually do into a deployment runbook someone else can execute: preconditions, ordered steps, a verification at every gate, a failure path that isn't hope.
Turn a release into a staged rollout plan you can defend — cohorts built from your risk rather than round numbers, gates that widen on evidence instead of a timer, and a written difference between pausing and rolling back.
Turn "are we good for Thursday?" into a checklist that can answer it — every item carrying the evidence behind it, a name, and an honest status, so the ship decision rests on what somebody checked rather than what nobody objected to.
Decide what deserves to wake somebody up. Plan monitoring from the journey that has to work rather than the components you happen to have — signals sorted into page, ticket and dashboard, and an alert for the failure that makes no noise at all.
"We do rolling deploys, so we have zero downtime" is true about the mechanism and often false about the change. Here is how to plan a zero-downtime deployment with AI: work out whether the old and new versions can actually run at once, across the database, the queue, and the traffic switch, before you ship.