AI Glossary
AI Lifecycle Management
AI Lifecycle Management is the practice of managing AI systems throughout planning, development, deployment, monitoring, maintenance, and retirement.
AI Lifecycle Management
Overview
Artificial intelligence systems require ongoing management from the moment they are planned until they are eventually retired.
This process is known as AI Lifecycle Management.
AI Lifecycle Management is the practice of managing AI systems throughout every stage of their lifecycle, including planning, development, testing, deployment, monitoring, maintenance, and retirement.
Rather than treating AI as a one-time project, organizations recognize that AI systems evolve over time.
Data changes.
Business requirements change.
Technology changes.
Regulations change.
A helpful way to think about AI lifecycle management is maintaining a commercial aircraft.
The aircraft is inspected, repaired, upgraded, and monitored throughout its operational life.
Organizations manage AI systems in much the same way.
AI lifecycle management supports AI Governance, AI Risk Management, and Model Monitoring by ensuring AI remains effective and responsible over time.
Why It Matters
Managing AI throughout its lifecycle helps organizations improve reliability, reduce risk, maintain compliance, and support continuous improvement.
Real-World Example
A company reviews every production AI model every six months to evaluate performance, update documentation, retrain models if necessary, and retire outdated systems.
Related Concepts
Related Articles
- How AI Models Go From Training To Production
- Why AI Models Need Continuous Monitoring
- What Is AI Risk Management?