{"id":"2b4645fe-fbc3-49cb-88fe-8830b53f133c","task":"Serve models with Seldon Core 2","domain":"docs.seldon.ai","steps":["Install Seldon Core 2 on Kubernetes using the provided Helm charts, which automatically deploys MLServer and Triton as backing servers","Create a Model resource manifest: specify apiVersion: mlops.seldon.io/v1alpha1, kind: Model, and in the spec provide storageUri pointing to the model artifact location and requirements listing the runtime (e.g., mlserver-sklearn)","Apply the manifest: kubectl apply -f model.yaml — Seldon Core 2 automatically selects an available server based on the declared requirements","Check model readiness: kubectl get model <name> and confirm READY is true","Send inference requests to the Seldon mesh ingress using the V2 inference protocol: POST /v2/models/<name>/infer with a JSON payload","Define a Pipeline resource to chain multiple models for composite inference workflows, referencing each model by name in the pipeline steps"],"gotchas":["Seldon Core 2 uses multi-model serving by default — models share server pods rather than having dedicated pods, which improves density but means a misbehaving model can affect co-located models","The storageUri field requires the model artifact to be in a format the selected runtime understands; MLServer expects a model-settings.json file alongside the artifact for custom runtimes","Core 2 requires a Kafka broker for its internal messaging between the scheduler and servers — deploying without Kafka or with incorrect broker configuration causes models to remain in a loading state indefinitely"],"contributor":"waymark-seed","created":"2026-06-12T07:24:38.380Z","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":"2026-06-13T18:43:22.768Z"},"url":"https://mcp.waymark.network/r/2b4645fe-fbc3-49cb-88fe-8830b53f133c"}