This lab is currently in Beta, content may be updated as we refine the material
LABINTERMEDIATE

Canary Model Deployment

Implement canary deployments for safe ML model updates using KServe.

60 minutes
ai-infrastructure/model-serving
Canary Model Deployment - Platform Engineering Hands-On Lab Icon

Lab Overview

🛠 Lab from the AI Platform Engineering Bootcamp. Used in Week 6. Bootcamp landing page: https://academy.tekanaid.com/bootcamps/ai-platform-engineering-bootcamp Parent course(s):

  • Week 6: Model Serving and Kubernetes for ML (slug: model-serving-106)

🟡 Beta bootcamp lab. Hands-on instructions, check scripts, and solve scripts are in place. Lab is part of the running Platform Assistant capstone arc that grows across all 8 weeks of the bootcamp.

This hands-on lab teaches you to implement canary deployments for ML models.

You'll learn to:

  • Deploy a baseline model version
  • Release a canary version with traffic splitting
  • Monitor both versions during rollout
  • Promote or rollback based on metrics

What You'll Learn

Deploy a baseline InferenceService version using KServe on Kubernetes

Configure traffic splitting to route a percentage of requests to a canary model version

Monitor inference latency and error rates for both the stable and canary versions simultaneously

Promote the canary to 100% traffic or roll back to the stable version based on observed metrics

Prerequisites

kserve-deployment

kubernetes-fundamentals

Technologies Covered

kuberneteskservecanarytraffic-splittingmodel-servingdeployment

Part of a Course

This lab is part of the Model Serving and Kubernetes for ML course

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