Deploying and scoring a Deep Learning model natively

Once a Deep Learning model is trained, it basically contains all the information about its structure, that is, its model weights, layers, and so on. For us to be able to use this model later in the production environment on new sets of data, we need to store this model in a suitable format. The process of converting a data object into a format that can be stored in memory is called serialization. Once a model is serialized in such a fashion, it's an autonomous entity and can be transmitted or transferred to a different operating system or a different deployment environment (such as staging or production).

However, once a model is transferred to a production environment, we must reconstruct the model parameters and weights in their original format. This process of recreation from the serialized format is called de-serialization.

There are some other ways to productionalize ML models as well, but the most commonly used method...