Technical GlossaryData Engineering and AI Infrastructure
Feature Consistency Check
A validation process that verifies whether training-side and serving-side feature values are produced with the same logic and definition.
Feature consistency checks are one of the core safeguards against train-serve skew. If the same feature is calculated differently in offline and online systems, model performance can silently degrade. Sample comparisons, hash validation, or definition-level matching checks may be used to detect this. In production AI systems, such checks act as a form of quality assurance.
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