Canary Model Deployment
Implement canary deployments for safe ML model updates using KServe.
Lab Overview
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
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