Canary Model Deployment
Implement canary deployments for safe ML model updates using KServe.
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
Send test traffic through the service and verify both the stable and canary versions return successful responses during the rollout, measuring request latency
Promote the canary to 100% traffic or roll back to the stable version based on observed metrics
Prerequisites
kserve-deployment
kubernetes-fundamentals
Technologies Covered
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