KairoAI Systems
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Production Delivery

Deployment using SageMaker Endpoints

Combine blue/green and multi-model techniques for scale.

What this covers

We showcase decision frameworks for single-model, blue/green, and multi-model endpoints plus automation hooks for each.

Implementation trail

  • Endpoint topology decisions
  • Deployment workflows
  • Traffic management
  • Cost optimization
  • Lifecycle governance

Choose the right endpoint pattern

  • Use dedicated endpoints for mission-critical, high-traffic models requiring isolated scaling.
  • Adopt multi-model endpoints for large model catalogs with bursty traffic to reduce idle cost.
  • Combine blue/green deployments with either topology to protect against regressions.

Automate deployments with confidence

  • Leverage CodePipeline to deploy endpoint configs and inference code with automated tests.
  • Integrate deployment approvals tied to Model Registry governance checkpoints.
  • Implement shadow testing by mirroring traffic to new endpoints before full cutover.

Optimize cost and performance

  • Enable auto scaling policies tuned to per-model latency goals.
  • Monitor model loading times in multi-model endpoints and pre-warm frequently accessed artifacts.
  • Archive unused models and clean up EBS volumes automatically to control spend.

Right-size your deployment stack

We design deployment strategies that balance risk, cost, and speed across single-model and multi-model endpoints.

Optimize your endpoint strategy