What is Blue-Green Deployment?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Maintain two identical serving environments: blue (currently serving production traffic) and green (idle, receiving the new model version). Deploy the updated model to the green environment, run automated validation tests (prediction accuracy on test inputs, latency benchmarks, health checks), and then switch the load balancer or DNS to route traffic from blue to green. Keep the blue environment running for 24-48 hours as an instant rollback target. Use Kubernetes namespaces or separate deployment sets to isolate environments. Tools like Argo Rollouts, Istio traffic management, or AWS CodeDeploy support blue-green patterns natively. Budget for 2x serving infrastructure during the transition period, reduced to 1.5x if using spot instances for the standby environment.

Run five validation gates before traffic switch: health checks confirming the new model loads successfully and responds to prediction requests within SLA latency targets, accuracy validation comparing predictions on a golden test set against expected outputs (fail if accuracy drops below threshold), smoke tests sending 50-100 representative production-like requests and verifying response format and value ranges, load tests simulating peak traffic patterns for 10-15 minutes to verify resource scaling handles production volume, and integration tests confirming upstream and downstream services communicate correctly with the new deployment. Automate all gates in your deployment pipeline with clear pass/fail criteria. If any gate fails, abort the deployment automatically and alert the ML engineering team with diagnostic details.

Maintain two identical serving environments: blue (currently serving production traffic) and green (idle, receiving the new model version). Deploy the updated model to the green environment, run automated validation tests (prediction accuracy on test inputs, latency benchmarks, health checks), and then switch the load balancer or DNS to route traffic from blue to green. Keep the blue environment running for 24-48 hours as an instant rollback target. Use Kubernetes namespaces or separate deployment sets to isolate environments. Tools like Argo Rollouts, Istio traffic management, or AWS CodeDeploy support blue-green patterns natively. Budget for 2x serving infrastructure during the transition period, reduced to 1.5x if using spot instances for the standby environment.

Run five validation gates before traffic switch: health checks confirming the new model loads successfully and responds to prediction requests within SLA latency targets, accuracy validation comparing predictions on a golden test set against expected outputs (fail if accuracy drops below threshold), smoke tests sending 50-100 representative production-like requests and verifying response format and value ranges, load tests simulating peak traffic patterns for 10-15 minutes to verify resource scaling handles production volume, and integration tests confirming upstream and downstream services communicate correctly with the new deployment. Automate all gates in your deployment pipeline with clear pass/fail criteria. If any gate fails, abort the deployment automatically and alert the ML engineering team with diagnostic details.

Maintain two identical serving environments: blue (currently serving production traffic) and green (idle, receiving the new model version). Deploy the updated model to the green environment, run automated validation tests (prediction accuracy on test inputs, latency benchmarks, health checks), and then switch the load balancer or DNS to route traffic from blue to green. Keep the blue environment running for 24-48 hours as an instant rollback target. Use Kubernetes namespaces or separate deployment sets to isolate environments. Tools like Argo Rollouts, Istio traffic management, or AWS CodeDeploy support blue-green patterns natively. Budget for 2x serving infrastructure during the transition period, reduced to 1.5x if using spot instances for the standby environment.

Run five validation gates before traffic switch: health checks confirming the new model loads successfully and responds to prediction requests within SLA latency targets, accuracy validation comparing predictions on a golden test set against expected outputs (fail if accuracy drops below threshold), smoke tests sending 50-100 representative production-like requests and verifying response format and value ranges, load tests simulating peak traffic patterns for 10-15 minutes to verify resource scaling handles production volume, and integration tests confirming upstream and downstream services communicate correctly with the new deployment. Automate all gates in your deployment pipeline with clear pass/fail criteria. If any gate fails, abort the deployment automatically and alert the ML engineering team with diagnostic details.