How to write a Custom Keras model so that it can be deployed for Serving

How to adapt custom Layers, Model, loss, preprocessing, postprocessing into a servable API

If the only Keras models you write are sequential or functional models with pre-built layers like Dense and Conv2D, you can ignore this article. But at some point in your ML career, you will find that you are subclassing a Layer or a Model. Or writing your own loss function, or needing custom preprocessing or postprocessing during serving. And at that point, you will find that you …

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