Hosting your own microservice on AWS
A classic way to surface your ML models is via a lightweight web service hosted on a server. This can be a very flexible pattern of deployment. You can run a web service on any server with access to the internet (roughly) and, if designed well, it is often easy to add further functionality to your web service and expose it via new endpoints.
In Python, the two most used web frameworks have always been Django and Flask. In this section, we will focus on Flask as it is the simpler of the two and has been written about extensively for ML deployments on the web, so you will be able to find plenty of material to build on what you learn here.
On AWS, one of the simplest ways you can host your Flask web solution is as a containerized application on an appropriate platform. We will go through the basics of doing this here, but we will not spend time on the detailed aspects of maintaining good web security for your service. To fully discuss this may require...