The coordinated processes for development, deployment, monitoring, maintenance, and retirement of AI systems to ensure ongoing compliance and risk control.
End-to-end governance covering ideation, requirements, design, testing, release, continuous monitoring (performance, bias, security), periodic retraining, version control, and secure decommissioning. Lifecycle management uses standardized workflows, checklists, and tooling (MLOps platforms) to enforce compliance at every stage, with audit trails capturing approvals, changes, and deprecation plans.
A financial-services firm uses an MLOps platform to manage its credit-scoring models: every new model version triggers automated impact assessments, bias checks, and security scans. Models retire automatically after one year unless revalidated - ensuring stale or unsupported systems never remain active in production.
