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Published by hamel.dev (Hamel Husain) (T1.5 source). The original is at https://hamel.dev/blog/posts/drift/.

Pipeline notes

The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.

Describes adversarial validation, a technique for detecting drift in AI/ML projects by training a binary classifier to discriminate between two datasets. If the classifier achieves meaningful predictive power (AUC ≥ 0.60), the datasets differ materially, indicating drift across training data, evaluations, or production inputs.

Concrete, low-infrastructure method for catching silent data drift between training, evaluation, and production — a common cause of misbehavior in LLM systems that often goes unnoticed.

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