Model lifecycle

The end-to-end stages of an AI model's existence as defined by the guidelines, encompassing design, dataset curation, development, testing, deployment, monitoring, maintenance, updating and decommissioning; the lifecycle framework informs risk assessments, documentation requirements and controls applied at each stage.

The sequence of stages for a model including design, development, testing, independent review/validation, approval, deployment, monitoring, maintenance/retraining (where applicable), change management and decommissioning/retirement, with documented controls at each stage.

The lifecycle concept enumerating model development phases (training, validation, deployment) used in the Strategy's glossary to align technical, testing, documentation and monitoring practices across institutions and to frame risk assessment and governance measures throughout an AI model's operational life.

The stages through which a generative model progresses: pre‑training (initial large‑scale training on broad data), fine‑tuning (task- or domain‑specific adaptation using additional data or objectives), and deployment (operational use in services or products). The vision uses these phases to structure risk identification, documentation, and governance measures.