Production path from notebooks to monitored services — feature pipelines, registry, and safe rollout for revenue-facing models.
Data science had strong notebooks but weak production paths. We built feature pipelines, a model registry, and deployment patterns so scoring services could ship with the same bar as any customer-facing API.
How we approached it
Feature paths
Batch and online feature patterns with shared entity keys, freshness checks, and reuse across models.
Registry & release
Versioned artifacts, canary deploys, and automated evaluation gates before traffic shifts.
Governance hooks
Approvals, audit trails, and responsible-AI checks appropriate to customer and revenue use cases.
What we delivered
- Feature store patterns and batch + online serving
- Model registry, deployment, and canary releases
- Drift monitoring, evaluation harness, and dashboards
- Governance hooks for approvals and audit trails
Outcomes
-
Monitored models with drift and business KPIs in one place
-
Repeatable release train instead of one-off handoffs
-
Room to add adjacent models without new bespoke plumbing
Representative stack: