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. The name comes from hardware testing: you power on a new circuit board and check if smoke comes out. If it doesn't, you proceed with more thorough
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). The switch happens at the router level, and rollback is instant: flip traffic back to blue. The testing challenge is making the cutover decision confidently: is green actually ready? The Blue-Green Testing