التحدي
An early ML pilot lacked the engineering discipline required for reliable scale: missing governance, deployment automation, feature management, monitoring and testing before it could be operated in production.
ما الذي قمنا به
Designed the MLOps architecture and delivered production-oriented batch ML foundations: feature store patterns, MLflow training and model registry, Databricks Asset Bundles CI/CD, Delta tables, batch inference, validation, monitoring and operational documentation.
النتيجة
Converted the pilot into an operationally robust delivery model, raising confidence in repeatability, governance, testing and responsible future scaling.
Helped operationalise a first ML pilot with the governance, automation and monitoring foundations needed to scale responsibly.
Reference patterns covered feature materialisation on Delta, MLflow-tracked training, registry and serving promotion gates, and monitoring hooks so batch inference could be validated before scale-out.