Is it possible to add feature extraction as part of a keras model, so when a user runs inference there is no need for computing those features as that part is done inside the model. Im looking for a sklearn Pipeline equivalent in tensorflow that can be saved into a saved model or tflite model.
1tf.keras.layers.preprocessing.Normalization) directly into your Keras model for an end-to-end pipeline. Use tf.keras.layers at the input of your model and chain them with your core network via the Functional API. Save the model (as SavedModel or TFLite) to ensure simplified and consistent deployment. Please refer to this document to know more about the Keras Processing Layers. Commented yesterday Start asking to get answers
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