AIGP term of the day: AI system development life cycle
AI system development life cycle
The iterative, structured process of moving from a problem/idea to an AI solution: planning/design, data collection/preparation, model development, testing/evaluation, deployment, monitoring/maintenance, decommissioning.
Study alongside
- AI registrar — An internal registry/inventory used to log AI incidents/issues and, more broadly
- AI regulatory sandbox — A supervised testing environment; the EU AI Act requires Member States to have at least
- AI system impact assessment (AIIA) — A structured assessment (per ISO/IEC 42005:2025) of an AI system's effects
- AI Verify — A Singapore-government AI governance testing framework/toolkit validating AI system
Every AIGP definition, in one place: the full glossary.
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