Applied machine learning

Models in service of decisions — ranking, forecasting, detection. We focus on the full loop, not just the model.

What it covers

Framing the decision first, then the model: feature pipelines, training, evaluation against a real baseline, deployment, and the monitoring that keeps a system honest after it ships.

How an engagement runs

We start with the simplest thing that could work — often a heuristic or a small model — and only add complexity when the baseline proves it pays. Every step ships with measurement attached.

What you get

A deployed model with the loop around it: retraining path, drift monitoring, and a written account of what it does and where it fails.

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