You’ve built ML models in your favorite IDE — but how do you operationalize them for regular, automated use? This guide walks through using AWS and Docker to automate recurring model training and deployment.

Prerequisites

  • AWS CLI installed and configured with appropriate IAM permissions
  • Linux/Unix development environment (optionally EC2)
  • Docker installed locally
  • Small dataset for testing
  • Pre-existing model code (this example uses scikit-learn LinearRegression)

Project Structure

project/
  build.sh          # Automates Docker image building and ECR push
  config.yaml       # Project configuration and hyperparameters
  Dockerfile         # Containerized environment with dependencies
  requirements.txt   # Python package dependencies
  run.py             # Utility for parsing configuration
  src/app.py         # Core ML model logic

Implementation Steps

Step 1: Local Development

Build and test locally first:

  • Build Docker image
  • Mount AWS credentials for S3 access
  • Test with sample data before touching the cloud

Step 2: Cloud Infrastructure

  • Configure IAM roles with necessary permissions (EC2, ECS, S3, Lambda)
  • Create ECR repository for image storage
  • Execute build.sh to push Docker images to ECR

Step 3: AWS Batch Configuration

  • Create job definitions specifying container image and parameters
  • Establish compute environment with appropriate instance types
  • Set up job queue connecting definitions to compute resources

Step 4: Job Execution

  • Submit jobs through AWS Batch console
  • Monitor execution and outputs

Advanced Automation

For more sophisticated orchestration, explore AWS Lambda and Step Functions. These enable event-driven workflows — trigger retraining on new data arrival, chain model training with evaluation and deployment, and handle failure scenarios gracefully.

Key Takeaway

To update code in production: re-run build.sh. It automatically prunes old images and deploys the latest version to ECR. Simple, repeatable, and version-controlled.