Note that this method has several drawbacks: * For traceability reasons, you should always have access to the custom objects that were used. You wouldn't want to put in production a model that you cannot re-create. * The object returned by tf.saved_model.load isn't a Keras model. So it's not as easy to use. For example, you won't have access to .predict() or .fit()