Shipping a model feels like the end of the project. The reality is that Model Deployment is just the beginning of a long operational lifecycle. Once an AI system is serving real traffic, it requires constant management. Models drift, usage patterns shift, and new versions must be introduced without breaking the application.
To manage this, teams need a system of record. They use a Model Registry or broader Model Catalogs to organize their assets. This is where Model Versioning and general Versioning become critical. By capturing Model Metadata, engineers establish a clear Model Lineage. If a model starts behaving strangely in production, you need to be able to trace that behavior back to the exact code and data that created it.
Visibility is just as important. Model Tracing lets you watch exactly how a request moves through the system. When you are ready to release a better model, you never just swap it out and hope for the best. You might run a Shadow Deployment to test the new version on live traffic without showing users the output. You might use a Canary Deployment to route a tiny percentage of real requests to the new model to see how it handles them. And if things go wrong, you need a tested Model Rollback procedure to instantly restore the previous version.
The articles in this section walk through this entire lifecycle. They cover the tools and strategies that keep AI systems stable from their first release to their eventual retirement.
