Model Lifecycle as a reviewable surface.
The lifecycle view treats AI in Data & Infrastructure as a managed capability: design, validation, deployment, monitoring, update, rollback, and retirement.
Model Lifecycle chapter.
Data & InfrastructureThe lifecycle view treats AI in Data & Infrastructure as a managed capability: design, validation, deployment, monitoring, update, rollback, and retirement.
What this page maps.
operating contentDesign record
Intended use, user group, workflow, data assumptions, acceptance criteria, and known limits.
Run state
Version, configuration, access, integration, logging, fallback, and support model.
Change route
Update class, approval route, release note, monitoring trigger, rollback route, and retirement decision.
Governance questions.
review logicWhat decision or record does this model lifecycle surface influence, and who owns that decision?
Which evidence is needed before routine use in Data & Infrastructure, and where is it retained?
What signal triggers review, restriction, escalation, or retirement?
Evidence-ready minimum record.
iFeed useSource anchors and claim boundary.
official firstThese anchors support the source layer for this page. iFeed interpretation remains separate from source facts and does not replace legal, regulatory, clinical, or product-specific advice.