https://stacksimplify.com/tags/model-registry/Recent content in Model Registry on StackSimplify | DevOps & Cloud Education by Kalyan ReddyHugo -- gohugo.ioen-usTue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/ml-governance-model-registry/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/ml-governance-model-registry/Your ML serving code should never know about version numbers. Ever. If your inference service loads fraud-detector-v47, you have a problem. What happens when v48 is ready? Code change. New deploy. Downtime risk. Now imagine this: your service always loads the model tagged @champion. (MLflow Model Registry docs) When v48 is promoted, the tag moves. Next request gets the new model. Zero code changes. Zero downtime. The Champion-Challenger Pattern Role Alias Purpose Champion @champion Currently serving production traffic Challenger @candidate Being evaluated against the champion The flow:https://stacksimplify.com/blog/mlflow-model-lifecycle/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/mlflow-model-lifecycle/How does an ML model actually get from training to production? If you’re a DevOps engineer stepping into MLOps, MLflow is the first tool you need to understand. It handles the entire lifecycle: tracking experiments, versioning models, and serving them in production. The 5-Step Lifecycle Here’s the full journey of a model, from code to production. Step What Happens DevOps Analogy Experiment Write training code, MLflow creates a “run” Starting a CI build Run Logs parameters, metrics, model files Build artifacts + test results Model Best run registered to Model Registry Pushing image to Container Registry Registry Versions (v1, v2, v3) with aliases (@champion, @candidate) Image tags (:latest, :staging, :prod) Serving API loads models:/fraud-detector@champion K8s Deployment pulling :prod tag Step 1: Experiment You write training code and run it.