A step-by-step walkthrough from Docker image to a live, serverless ML application running in the cloud Introduction Deploying a machine learning project is often where things get humbling. You've trained a model, built a pipeline, maybe even wired up a slick dashboard, and then you stare at your terminal wondering how to get any of it onto a real server that other people can access. I've been there. In this article, I'll walk you through exactly how I deployed a real-time machine learning application on AWS, from pushing a Docker image to ECR, to running two live containers on ECS Fargate, to watching my dashboard update in real time. Every command here actually worked. I'll explain what each step does, why it matters, and what you should expect to see as output. Whether you're deploying your first ML project or looking for a reference you can actually trust, this guide is for you.…