How to properly ship and deploy your machine learning model

As a data scientist, training your machine learning model is only a part of providing a solution for the client. Besides generating and cleaning the data, selecting and tuning the algorithms, you also need to deliver and deploy your results so that it is usable in production. This is a large field in itself with constantly evolving tools and standards. In this post, my goal is to present a practical guide on how to do this using the currently available state of the art tools and best practices. We are going to build a system which can serve as a starting point for your deployment tasks, regardless of the actual machine learning problem itself! Instead of a minimal app barely scratching the surface of the used tools, I aim to introduce best practices and demonstrate advanced features, so that you don't have to learn the hard way. Learning from your own mistakes is nice, but thinking ahead and not committing those mistakes is much better. To create our deployment-ready application, we will use two tools as our main building blocks: Docker and FastAPI.