Serving at Scale - Cloud Deployment with AWS

In the previous tutorial, you successfully packaged your machine learning model and its preprocessing pipeline into a portable, self-contained Docker container. This was a crucial step, creating a standardized artifact that runs consistently in any environment. However, a model running on your local machine can't serve real-world applications. To unlock its true value, we need to deploy it to the cloud, making it scalable, reliable, and accessible to users and other services 24/7.

This tutorial will guide you through that final, critical phase: deploying your containerized ML application to Amazon Web Services (AWS). We will build a production-grade workflow, starting with storing your model artifacts in Amazon S3 for robust versioning. Next, you'll learn to push your Docker image to the Amazon Elastic Container Registry (ECR), a secure and managed repository. Finally, we will orchestrate the entire deployment using Amazon Elastic Container Service (ECS), which will launch and manage your API on a cloud server. By the end, you will have a live, publicly-accessible API endpoint running on AWS, ready to serve predictions to the world.