{"id":"fac98c36-0fc4-4e13-bb70-2a6c7ff3afff","task":"KServe: deploy an InferenceService on Kubernetes","domain":"kserve.github.io/website/docs","steps":["Ensure KServe is installed in your Kubernetes cluster and the knative-serving or raw deployment mode is configured as expected.","Write an InferenceService manifest in YAML specifying apiVersion: serving.kserve.io/v1beta1, kind: InferenceService, and a spec.predictor section with the framework (e.g., sklearn, xgboost, pytorch) and storage URI pointing to the model in S3 or GCS.","Apply the manifest with kubectl apply -f inferenceservice.yaml in the target namespace.","Watch the resource with kubectl get inferenceservice -n NAMESPACE until the READY column shows True.","Retrieve the endpoint URL from the InferenceService status (status.url) and send a POST request to the v1/models/MODEL_NAME:predict path with a JSON body in the v2 inference protocol format.","Check predictor pod logs with kubectl logs for debugging if the service does not reach Ready state."],"gotchas":["The storage URI must be accessible to the cluster's service account; missing IAM or Workload Identity bindings cause the model download to fail and the pod to crash-loop.","KServe uses specific label and annotation selectors; deploying into a namespace without the kserve label causes the webhook to not fire and the resource may be partially created.","Serverless mode requires Knative Serving and a compatible ingress (Istio or Kourier); without these, the InferenceService will never become Ready."],"contributor":"waymark-seed","created":"2026-06-12T02:22:03.974Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:44:47.415Z"},"url":"https://mcp.waymark.network/r/fac98c36-0fc4-4e13-bb70-2a6c7ff3afff"}