Model Lifecycle Management addresses the ongoing maintenance of AI models from initial deployment through to retirement. This includes version control (tracking which model version is deployed where), scheduled retraining (updating models with new data to maintain performance), validation and testing before deployment of updates, performance monitoring to trigger retraining when needed, managing rollback procedures when updates cause problems, and eventually decommissioning obsolete models. It’s analogous to software lifecycle management but with additional considerations around data dependencies and model performance degradation.
Unlike traditional software, AI models can become less accurate over time even without any code changes—this is known as model drift. Real-world conditions change, new patterns emerge in data, or the relationship between inputs and outputs shifts. This dimension assesses whether you manage model versions, retraining schedules, and decommissioning processes.
Why It Matters
Models that are not maintained degrade over time, leading to inaccurate or biased outputs.
Maturity Levels
| Basic | Standard | Advanced | Leading |
|---|---|---|---|
| Models are never retrained or validated after deployment. | Ad-hoc updates when issues arise. | Formal lifecycle management with scheduled retraining and validation. | Continuous model revalidation with automated rollback and retirement processes. |
📥 Related Resources & Templates
Downloadable templates, examples, and frameworks to help you implement this dimension.
Model Retirement Policy
PremiumPolicy template for retiring AI models, including decommissioning procedures, data handling, and transition planning.
Model Retraining Schedule
PremiumTemplate for planning and tracking model retraining schedules, triggers, and validation processes.
Model Validation Report
PremiumTemplate for documenting model validation results, performance metrics, and approval for deployment.