In this article, which is the first in the series, we explore how we can prepare a deep learning model for production and deploy it inside of Python Web application. This is just the first step in the long journey. In fact, deployment of Deep Learning models is an art for itself. This task requires goes beyond data science knowledge and engages lot of software development and DevOps skills. Why should you care about all this? Well, at the moment one of the most valued role in data science teams are Machine Learning engineers. This role gathers best of both worlds. These engineers don’t have to know only how to apply different Machine Learning and Deep Learning models to a proper problem, but how to test them, verify them and finally deploy them as well. Having a person that is able to put deep learning models into production became huge asset to any company. This is, in general, main type of services that Rubik’s Code provides. In order to become successful Machine Learning engineer, you need to have variety of skills that are not focused only on the data. If we compare the amount of code that is written for Machine Learning models, and the rest of the code that supports testing and serving that model, it looks something like this: