Model Lifecycle Management
The end-to-end process of managing a machine learning model from conception through development, deployment, monitoring, maintenance and eventual retirement.
In plain language
Managing an AI model from creation to retirement; building, testing, deploying, monitoring, updating and eventually replacing it. Like overseeing any product through its complete lifespan.
Why this matters
Governance applies to every stage of the AI lifecycle. Your framework must define requirements, checkpoints and responsibilities for conception, development, deployment, monitoring and retirement to ensure consistent oversight.
Relevance
GovernanceLifecycle management is the backbone of AI governance, requiring documented processes and accountabilities at every stage from design to retirement.
Related terms
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