Soluções
Scale-up de SaaS B2BPlataforma ML para scoring de clientes
Caminho de produção dos notebooks para serviços monitorizados — features, registo e rollout seguro para modelos orientados a receita.
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.
Como abordámos
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.
O que entregámos
- Padrões de feature store e serving batch + online
- Registo de modelos, implantação e releases canary
- Monitorização de deriva, avaliação e dashboards
- Pontos de governação para aprovações e trilhos de auditoria
Resultados
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 representativo