Model Lifecycle Governance

Model lifecycle governance ensures that artificial intelligence models are developed, validated, deployed, monitored, and retired through a controlled and accountable process. Without formal governance, models may bypass approvals, introduce unmanaged risks, or remain operational beyond their intended purpose. In this lesson, learners will examine lifecycle stages, governance checkpoints, promotion gates, change management practices, version control, and retirement procedures. Understanding lifecycle governance enables organizations to maintain traceability, support regulatory compliance, strengthen accountability, and ensure that AI systems remain trustworthy and auditable throughout their operational existence.