Automate Model Deployment with GitHub Actions and AWS

This article was published as a part of the Data Science Blogathon. In a typical software development process, the deployment comes at the end of the software development life cycle. First, you build software, test it for possible faults, and finally deploy it for the end user's accessibility. The same can be applied to machine learning as well. In a previous article, I described how we could build a model, wrap it with a Rest API, containerize it, and finally deploy it on cloud services.