The slow part was evidence, not inference.
Research teams trained models in notebooks, platform engineers rebuilt environments by hand, and quality reviewers assembled validation evidence from screenshots and spreadsheets. A release crossed six queues before production.
Training and serving dependencies diverged between research, validation and production.
Reviewers could not reproduce a model run from the submitted evidence bundle.
The previous artifact existed, but its features and runtime contract were not versioned together.
One release object carried code, data and evidence.
Every candidate became a signed release manifest: Git SHA, container digest, feature-view version, dataset snapshot, evaluation report, model card and approval state.
Kubeflow component image + locked dependencies
Metrics, lineage, signatures and model card
Automated thresholds + accountable approval
Shadow traffic before weighted promotion
Feature and prediction telemetry
Three choices made the platform operable.
AUC alone could not approve production. Data freshness, calibration, latency and explainability became first-class gates.
OpenLineage events were required before a candidate could enter validation.
Teams started from opinionated pipelines while retaining controlled escape hatches.
Review time collapsed because evidence became automatic.
| Measure | Before | After |
|---|---|---|
| Model release lead time | 4–6 weeks | Same business day |
| Environment rebuild | 2–3 days | Immutable image |
| Validation evidence | Manual packet | Generated per run |
| Rollback readiness | Best effort | 18 minutes tested |
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