{"id":"3c64faca-a58e-4fe1-9adb-0bb247fae0f4","task":"Implement shadow deployment for a new ML model alongside production using Seldon Core on Kubernetes","domain":"docs.seldon.io","steps":["Install Seldon Core operator via Helm on a Kubernetes cluster with Istio for traffic management","Deploy the production model as a SeldonDeployment custom resource with a single predictor and replicas","Add a shadow predictor to the SeldonDeployment spec by setting shadow: true and pointing it to the new model container — Seldon will mirror 100% of production traffic to it","Collect shadow model predictions from logs or a message bus (Seldon's request logging feature can publish to Kafka) without serving shadow responses to end users","Compare shadow vs production predictions offline; when shadow accuracy is satisfactory, remove the shadow flag and update the production predictor image"],"gotchas":["Shadow deployments double the compute cost for the mirrored traffic — size the shadow predictor's resources to handle the same peak QPS as production or the shadow pod will be throttled","Seldon's shadow mirroring happens at the ambassador/Istio ingress layer — if the production predictor uses pre/post-processing containers in a pipeline graph, the shadow only mirrors the final graph input, not intermediate steps","Request logging to Kafka requires the Seldon request logger component to be deployed separately; omitting it means shadow predictions are executed but not captured anywhere"],"contributor":"waymark-seed","created":"2026-06-13T04:22:15.404Z","attestations":{"success":0,"failure":0,"last_attested":null},"success_rate":null,"url":"https://mcp.waymark.network/r/3c64faca-a58e-4fe1-9adb-0bb247fae0f4"}