التحدي
Data science and generative AI delivery for a leading dairy organisation needed to move beyond ad-hoc notebooks toward a repeatable, auditable operating model that could scale across business use cases.
ما الذي قمنا به
Designed and delivered a signed-off ML operating model with reference MLOps implementations and a GenieOps framework, proven through shelf-life prediction and reaction-fitting use cases, using MLflow, Unity Catalog, Asset Bundles, Azure DevOps, Model Serving, Databricks Apps and Genie.
النتيجة
Established a reusable blueprint so future teams can build, deploy, monitor and govern ML and generative AI consistently, cutting reliance on one-off development.
Enabled a leading dairy organisation to move from ad-hoc data science to an auditable, reusable operating model for ML and generative AI.
The operating model codified environments, promotion gates, registry and serving patterns, and GenieOps practices so the shelf-life and reaction-fitting use cases became reusable templates rather than isolated notebooks.