Custom ML & Data

Custom ML is for problems a general LLM should not own: demand forecast, credit or risk scores, quality detection, churn. We start with the decision the score will change and the data you can legally use. If the data is not there, we build the pipeline first and delay the model. A notebook on an export is not a product.

Pipelines are scheduled, monitored, and owned. Features are documented. Training jobs are repeatable. Evaluation matches the business metric — not only AUC on a leaderboard. Bias and drift checks are sized to the harm of the decision. We will recommend not to model when a rule is enough.

Deployment is a service, a batch table, or an embedded score in the app that already runs the process. Retraining cadence and rollback of a bad model are written down. The data store and jobs live in your cloud account.

You get a score that a system consumes, lineage you can explain, and an owner for when the metric moves. This is engineering, not a slide titled 'AI strategy'. Feature definitions, training windows, and the decision the score is allowed to change are written so a regulator, a credit officer, or a plant manager can ask 'why this number' and get an answer that is not a notebook screenshot.