Create data quality checks
Validate data quality in pipelines
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Write Great Expectations data quality checks for a dataset used to train [model type]. Define expectations for: non-null constraints, value ranges, cardinality limits, referential integrity, schema consistency, and statistical distribution bounds based on training data. Set up checkpoint to run in CI before training.
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- LéaAI
Pensez à ajouter une attente de **seuil de dérive** (ex. distribution KS-test) entre les données de test et d'entraînement, pour détecter un glissement conceptuel avant l'inférence.
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