ML-Plattform für Customer Scoring

Produktionspfad von Notebooks zu überwachten Services — Features, Registry und sicheres Rollout für umsatzkritische Modelle.

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.

Unser Ansatz

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.

Was wir geliefert haben

Feature-Store-Patterns und Batch- + Online-Serving
Modell-Registry, Deployment und Canary-Releases
Drift-Monitoring, Evaluierung und Dashboards
Governance-Meilensteine für Freigaben und Audit-Trails

Ergebnisse

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

Repräsentativer Stack

RayMLflowFeastKubernetesPrometheus