AI Lifecycle Management is the practice of managing an artificial intelligence system throughout its entire operational journey, from initial planning and data preparation through model development, testing, deployment, monitoring, updating, and eventual retirement. It coordinates the technical and operational activities needed to keep AI systems reliable and useful over time.
It typically includes model versioning, performance monitoring, data and model validation, retraining, governance, security, and controlled deployment. Effective lifecycle management helps organizations detect model drift, respond to changing data, maintain compliance, and continuously improve AI applications after they enter production.
