Why architecture matters here

Pipeline v2 fails on missing feature store or registry. Architecture matters because platform + governance + cost combine.

Advertisement

The architecture: every piece explained

The top strip is components. Raw data lake. Feature store. Training orchestrator. Model registry.

The middle row is lifecycle. Serving. Monitoring. Retraining trigger. Governance.

The lower rows are experience. Cost / compute. User portal. Ops — audit + rollback + support.

ML pipeline v2 — feature store + registry + orchestration + monitoring + governancemature ML platformRaw data lakeIceberg / Delta / S3Feature storeonline + offlineTraining orchestratorKubeflow / RayModel registryversioned artifactsServingbatch + onlineMonitoringdrift + performanceRetraining triggerdrift or scheduleGovernancelineage + approvalCost / computeGPU + spot + quotasUser portalself-serviceOps — audit + rollback + supportservemeasureautogovernbudgetportalportaloperateoperate
Mature ML platform pipeline v2.
Advertisement

End-to-end flow

End-to-end: DS ingests via lake. Features materialized in store (offline + online). Training orchestrated on GPU quota; registers artifact. Serving deploys via GitOps. Monitoring detects drift; triggers retraining. Governance approves before prod.