Model packaging

After the trained model has been tested in the previous step, the model can be serialized into a file to be exported to the test or the production environment. Serialized files come with compatibility challenges, such as model interoperability, if not done right. Model interoperability is a challenge, especially when models are trained using different frameworks. For example, if model 1 is trained using sklearn and model 2 is trained using TensorFlow, then model 1 cannot be imported or exported using TensorFlow for further model fine-tuning or model inference.

To avoid this problem, ONNX offers an open standard for model interoperability. ONNX stands for Open Neural Network Exchange. It provides a serialization standard for importing and exporting models. We will use the ONNX format to serialize the models to avoid compatibility and interoperability issues.

Using ONNX, the trained model is serialized using the skl2onnx library. The model is serialized as the...