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Stop relying on implicit Keras exports. Learn how to use TensorFlow SavedModel signatures to create a stable contract between your training pipeline and production inference.
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.
Goal Deploy a small Keras image‑classification service behind TensorFlow Serving and update the model to a new version without interrupting incoming inference requests. Constraints The serving instance must continue to accept requests during the swap, and the update mechanism should rely only on TensorFlow Serving’s built‑in version‑alias feature rather than
keras