Pytorch to Keras using ONNX

Model Deployment is the method by which you integrate a machine learning model into an existing production environment to make practical business decisions based on data. It is one of the last stages in the machine learning life cycle and can be one of the most cumbersome. Model deployment is probably the most important part of the Machine Learning model lifecycle but still, the least studied one. Most of the courses out there around the ML/DL universe teach how to explore data, engineer the features, train the model, and generate predictions. But they miss the most important part: what to do after that? Apart from the models developed for learning or for Kaggle competitions, all other models are built to generate revenue, and if you don't deploy a model into production then there's no one using it and thus no revenue.