Solutions
Éditeur SaaS B2B en scale-upPlateforme ML pour scoring client
Chemin production des notebooks aux services surveillés — features, registre et déploiement prudent pour des modèles orientés revenus.
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.
Notre approche
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.
Ce que nous avons livré
- Patterns feature store et serving batch + online
- Registre de modèles, déploiement et sorties canary
- Surveillance de dérive, dispositif d’évaluation et tableaux de bord
- Points de gouvernance pour validations et pistes d’audit
Résultats
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
Stack représentatif