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Train-serve skew usually comes from duplicated preprocessing, not the model. Keras preprocessing layers move normalization and tokenization inside the model graph so the saved artifact handles raw data itself.
Learn how to combine scikit‑learn's Pipeline with ColumnTransformer to preprocess mixed numeric and categorical data safely and reproducibly.
Learn how to embed data preprocessing directly into a Keras model using preprocessing layers, ensuring consistent transforms at training and serving time.
Goal: ensure repeatable preprocessing pipeline that handles numeric and categorical features while preserving ability to inspect feature importance after training. Constraint: need to avoid data leakage during cross-validation and maintain consistent transformation across folds. Uncertainty: whether embedding ColumnTransformer inside a Pipeline offers any ad
keras
tensorflow