Model Lifecycle Management

Models have a lifecycle that extends well beyond their initial deployment. They're developed, validated, deployed, monitored, and eventually retired or replaced. Managing this lifecycle systematically means defining clear stages and criteria for transitions between them. A typical lifecycle includes development (experimentation and training), staging (testing in a production-like environment), production (serving live traffic), and retirement (graceful decommissioning). Each transition should have defined quality gates - minimum performance thresholds, required tests, approval workflows, and documentation standards. In practice, many organisations skip the formal lifecycle management and deploy models ad hoc, leading to situations where nobody knows which models are running, which are still relevant, or which should have been retired months ago. As the number of deployed models grows - and most organisations will see this number increase significantly - the lack of lifecycle management becomes a serious liability. Shadow models that nobody maintains continue consuming resources and potentially serving poor results. Retired models that aren't properly decommissioned may still be accessed by downstream systems. Clear ownership, regular reviews, and systematic retirement processes prevent the accumulation of model debt - the AI equivalent of technical debt - that slows down future development and creates risk.