Also known as: AI Life Cycle Β· Artificial Intelligence Life Cycle
The AI Life Cycle is the evolution of an AI system from planning and design through data work, model development or adaptation, verification, deployment, operation, monitoring, and retirement. The phases are often iterative and may not occur in a strictly linear order.
An AI system lifecycle typically involves several phases that include: to plan and design, collect and process data, build model(s) and/or adapt existing model(s) to specific tasks, test, evaluate, verify, and validate, make available for use/deploy, operate and monitor, retire/decommission. These phases often take place in an iterative manner and are not necessarily sequential. The decision to retire an AI system from operation may occur at any point during the operation and monitoring phase.
According to the OECD, the AI system lifecycle involves the following phases which take place iteratively and are not necessarily one after the other: i) 'design, data and models' (encompassing planning and design, data collection and processing, as well as model building); ii) 'verification and validation'; iii) 'deployment'; and iv) 'operation and monitoring'.