Serving at Scale - Cloud Deployment with AWS
Learn to deploy a containerized ML model to the cloud. This guide covers pushing artifacts to S3, storing your Docker image in ECR, and orchestrating deployment with AWS ECS and EC2.
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.
Tutorial Goals
- Set up an S3 bucket for DVC remote storage and push artifacts.
- Push a Docker image to the Amazon Elastic Container Registry (ECR).
- Modify the Dockerfile to pull artifacts from S3 during the build.
- Create and configure an AWS ECS cluster with an EC2 instance.
- Define an ECS Task and Service to deploy the container.
- Test the live API endpoint running on AWS.
- Learn to tear down all cloud resources to manage costs.