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AUTOMATION & AI

ML Model Development

Forecasting, classification and anomaly detection built in Fabric notebooks and deployed to Azure ML.

Azure MLMicrosoft FabricPythonMLflow
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What this service is

Most ML projects fail in production, not in the notebook. We build models that survive deployment — with the MLOps, monitoring and retrain logic baked in from the start.

How we deliver it

Each engagement follows a clear, repeatable shape.

01
Frame the problem in business terms, then in ML terms — in that order.
02
Build and back-test in Fabric notebooks against real outcomes.
03
Productionise on Azure ML with proper versioning and CI/CD.
04
Wire monitoring and scheduled retrains so the model stays current.

What you get

The tangible outputs you'll have at the end.

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