Summary

Data scientists often play a supporting role in the model deployment and scoring aspects. However, in some companies (or smaller data science projects where there may not be a fully staffed engineering or ML-Ops team), data scientists may be asked to do such tasks. This chapter should be helpful in preparing you for doing both test and experimental deployments, as well as integration with end user applications.

We have seen in this chapter how PyTorch Lightning can be easily deployed and scored to be consumed via a REST API endpoint with the help of a Flask application. We have seen how we can do so both natively via checkpoint files or via a portable file format such as ONNX. We have seen how different file formats such as ONNX can be used to aid the deployment process in real-life production situations, where multiple teams may be using different frameworks for training the models.

Looking back, we started our journey with an introduction to our first Deep Learning...