This article continues a series of articles tackling the most frightening idea in the world of production ML: putting the damn thing in production. In previous stories, we saw two different approaches to designing a Machine Learning (ML) powered application. First, we examined why you'd want to keep your model within your web server and why you should not do it. Keeping your model side-by-side with your core business logic is a must as you experiment with different ideas and want quick feedback from a trusted circle of testers, but it falls short when deploying in production. The first solution we discussed is a very simple technique that permits us to separate the model from our web server.