Model lifecycle stages

Distinct phases in an AI model's existence as defined by the Strategy: training (building models from data), validation (evaluating performance and risks), and deployment (operational use in production environments). The lifecycle framing supports governance actions such as testing, monitoring, documentation, and risk mitigation at each stage.