This is the capstone project. You already know how to train a model, wrap it in an API and package it with Docker. Now you automate the whole path from code to production. In an end-to-end deployment, every change you push to GitHub is automatically tested, built into a Docker image and deployed, with no manual steps.
The lessons start with GitHub Actions and the basics of CI/CD, including building and pushing a Docker image. Next you automate the workflow for a dockerized Flask app, with tests that run before anything ships. The last lesson deploys an end-to-end ML application on AWS using a CI/CD pipeline with ECR and EC2.
For your own version, take the FastAPI and Docker app from the previous project, add a few tests and create a GitHub Actions workflow that builds and deploys it. Cloud accounts can cost money, so set a budget alert in AWS and shut down resources when you finish.
Learn the core concepts of GitHub Actions and build a CI/CD pipeline that builds a Docker image and pushes it to a registry.
Video 32minSet up a Flask project with Docker, write the GitHub Actions workflow file and test the Docker image it builds.
Video 43minDeploy a machine learning application on AWS using a CI/CD pipeline: GitHub Actions, an ECR repository and an EC2 instance running Docker.
Video 45min