Model Lifecycle Management

The end-to-end process of managing a machine learning model from conception through development, deployment, monitoring, maintenance and eventual retirement.

Managing an AI model from birth to retirement; building it, testing it, deploying it, monitoring it, updating it and eventually replacing it. Like managing any product through its full lifespan.

Governance applies to every stage of the AI lifecycle, from design through to retirement. Your governance framework must define requirements, checkpoints and responsibilities for each phase to ensure consistent oversight and risk management.