All AI models have an end of life. Failing to retire them safely can introduce security, compliance, and operational risks.
Safe Model Retirement is a practical guide for model owners and technical leaders responsible for deciding when and how to retire, replace, or sunset machine learning models. The book focuses on controlled offboarding rather than abrupt shutdowns, ensuring continuity, accountability, and regulatory alignment.
This guide addresses the often-overlooked final phase of the model lifecycle and provides clear decision criteria for action.
Readers will learn how to:
- Identify technical, business, and risk signals that trigger retirement
- Plan safe transitions to replacement models or manual processes
- Manage data retention, archiving, and deletion obligations
- Decommission models without disrupting dependent systems
- Update documentation, ownership, and governance records
- Reduce residual risk from dormant or forgotten models
Written in a practical and accessible style, this book helps organizations close the loop on AI lifecycle management and avoid hidden liabilities.
