Putting Machine Learning in Production

In this article, we will discuss how to go from the research phase to the production phase for ML projects and what are the different options to do so. If you try to have your training and server code in the same repository you would probably end up with a big mess that is hard to maintain. Training models and serving real-time prediction are extremely different tasks and hence should be handled by separate components. Last but not least, there is a proverb that says "Don't s**t where you eat", so there's that too. Thus, a better approach would be to separate the training from the server.