KairoAI Systems
Back to Playbooks
Deployment Excellence

Blue/green deployment process for SageMaker

Execute safe model rollouts with CodePipeline and CodeDeploy.

What this covers

This playbook shows how to pair infrastructure-as-code with release engineering so you never ship an untested model to production.

Implementation trail

  • Pipeline design
  • Environment provisioning
  • Traffic shifting strategy
  • Automated validation
  • Rollback readiness

Codify the release pipeline

  • Use CodePipeline to orchestrate source, build, test, and deploy stages triggered by Model Registry approvals.
  • Package endpoint configuration as CloudFormation templates to keep environments consistent.
  • Integrate security scans for container images and inference scripts before deployment.

Automate blue/green environments

  • Provision a parallel green endpoint with identical instance types, autoscaling policies, and encryption settings.
  • Synchronize environment variables and feature toggles through AWS Systems Manager Parameter Store.
  • Keep blue and green endpoints warm by replaying representative traffic before cutover.

Shift traffic with confidence

  • Use CodeDeploy to gradually adjust traffic weights, monitoring latency and error rates at each increment.
  • Define bake times that allow downstream caches or consumers to stabilize before final cutover.
  • Trigger automated rollbacks on failed health checks or manual aborts from release managers.

Modernize your ML releases

We deploy blue/green automation, synthetic tests, and governance workflows so your teams iterate faster without sacrificing reliability.

Engineer safer rollouts