https://ops.docs.72602.space/kubernetes/serverless/kserve/canary/example/index.html Create the InferenceService Follow the First Inference Service tutorial. Set up a namespace kserve-test and create an InferenceService. After rolling out the first model, 100% traffic goes to the initial model with service revision 1. kubectl -n kserve-test get isvc sklearn-iris Expectd Output NAME URL READY PREV LATEST PREVROLLEDOUTREVISION LATESTREADYREVISION AGE sklearn-iris http://sklearn-iris.kserve-test.example.com True 100 sklearn-iris-predictor--00001 46s 2m39s 70s Apply Canary Rollout Strategy Add the canaryTrafficPercent field to the predictor component Update the storageUri to use a new/updated model. kubectl apply -n kserve-test -f - <<EOF apiVersion: "serving.kserve.io/v1beta1" kind: "InferenceService" metadata: name: "sklearn-iris" namespace: kserve-test spec: predictor: canaryTrafficPercent: 10 model: args: ["--enable_docs_url=True"] modelFormat: name: sklearn resources: {} runtime: kserve-sklearnserver storageUri: "gs://kfserving-examples/models/sklearn/1.0/model-2" EOF After rolling out the canary model, traffic is split between the latest ready revision 2 and the previously rolled out revision 1. Hugo en