AI system lifecycle

The end-to-end stages of an AI system including data collection and curation, model training and validation, deployment, monitoring, and maintenance; used to structure obligations and risk controls across development and operational phases.

The lifecycle stages (design, train, test, deploy, monitor, retire) applied to AI systems to structure risk assessment, testing, governance and ongoing monitoring; guidance aligns these stages with OECD lifecycle concepts for operational controls.

The end-to-end sequence of stages an AI system undergoes, including conception, design, data collection, development, testing, deployment, operation, monitoring, maintenance, and eventual retirement or decommissioning. Lifecycle framing guides where safety, security, governance, and accountability measures should be applied.

The full set of phases for an AI system, including design and development, deployment, operation and monitoring, and retirement/decommissioning, encompassing activities needed to manage the system across its operational life.

A comprehensive process spanning from initial research and design to procurement, deployment, use, maintenance, and termination.

A structured sequence of stages for AI systems defined by the strategy — plan & design; data collection and processing; model building/adaptation; testing/validation; deployment; operation/monitoring; and decommissioning — used to organise governance, documentation, risk assessments and oversight across an AI system's existence.

The full range of activities and processes that are relevant to an AI system, from its conception and design to its deployment, operation, and decommissioning. This includes, but is not limited to, data collection, processing, model development, testing, validation, deployment, monitoring, and maintenance.

The complete progression of an AI system from its initial conception and design through data acquisition, model development, verification, validation, deployment, operational monitoring, incident handling, and eventual retirement. This comprehensive approach ensures that all stages of an AI system's existence are subject to structured management and oversight, particularly concerning risk.