ML Testing
You've trained a new model. Offline metrics look better than the current production model. But will it actually perform better with real users and real traffic? Shadow deployment answers that question without risk. You run the new model in parallel with production, compare their predictions, and collect real-
Testing Strategies
Canary deployments and shadow deployments both use production traffic to validate new code before full rollout. Both reduce release risk compared to big-bang deployments. But they expose different amounts of risk to real users and answer different questions — choosing the wrong one can either leave dangerous bugs undetected or
Testing
Deploying a new ML model version is riskier than deploying a new API version. A bad API deployment returns errors — observable, alertable, rollback-triggering. A bad model deployment returns predictions that look valid but are wrong. Accuracy can degrade 20% before a business metric moves enough to alert. By then
Smoke Testing
A "smoke test" is a quick verification that the most critical functions of a system work after a change.
Deployment
Progressive delivery automatically routes a small percentage of traffic to a new version, analyzes metrics, and either advances the rollout or triggers an automatic rollback. The system replaces human judgment in the deployment pipeline — which means the testing focus shifts to validating that the automation itself is correctly configured. Progressive
Deployment
Blue-green deployment reduces downtime risk by running two identical production environments — blue (live) and green (new).