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AI & Intelligence · Service 13

Data & ML Engineering

The data pipelines, feature stores, and MLOps foundations that make AI reliable — from raw sources to monitored models in production.

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Data from ERP, CRM, IoT, and files is ingested and cleaned into a feature store, trained into a model, approved in a registry, and deployed as an API, while a monitor detects drift and triggers a retrain loop with lineage and access control throughout.
How Data & ML Engineering works in practice.

Problems we solve

  • Data scattered across systems with no trusted single view
  • Models trained once on a laptop and never maintained
  • No monitoring for drift, accuracy, or data quality

What we deliver

  • Ingestion and transformation pipelines with quality checks
  • Feature stores, model registries, and versioning
  • CI/CD for models with staged rollouts
  • Drift monitoring, lineage, and access control

Engagement

Platform foundations first, then model delivery on top of them.

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Next step

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