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Most ML projects die in production, not in the notebook. We focus on the boring parts — feature stores, model registries, monitoring — that turn a prototype into a system the business can depend on.
Most ML projects die in production, not in the notebook. We focus on the boring parts — feature stores, model registries, monitoring — that turn a prototype into a system the business can depend on.
Hard-nosed evaluation: does ML actually solve this better than rules? What is the cost of being wrong?
Classical ML where it suffices. Deep learning where it earns its keep.
Feature stores, experiment tracking, model registries, deployment pipelines, drift monitoring.
Anomaly detection, segmentation, predictive maintenance, demand forecasting — with confidence intervals and human-in-the-loop fallbacks.
Insurance analytics platform. Predictive customer-segmentation models in Jupyter Notebooks. Designed dashboards and held workshops.
Built Azure Data Lake for IoT data. Implemented anomaly-detection ML models with visual alerting.
PoC anomaly detection on industrial telemetry. Visualization concepts with Power BI.
First conversation is always free.