Build Secure MLOps on AWS with Terraform, GitHub, and SageMaker
This article outlines the implementation of a secure MLOps platform, integrating Terraform, GitHub, and GitHub Actions for automated machine learning (ML) use case deployments on AWS. MLOps is defined as the convergence of people, processes, and technology to efficiently productionize ML, emphasizing reproducibility, robustness, and end-to-end observability. The platform leverages a multi-account setup with strict security constraints, adopting CI/CD best practices where user interaction is primarily through code commits.
AA core benefit is the streamlined journey from ML model development to robust deployment across experimentation, preproduction, and production environments. Terraform serves as the infrastructure as code (IaC) tool for standardizing AWS infrastructure, while GitHub and GitHub Actions facilitate CI/CD. The solution integrates custom Amazon SageMaker Projects templates, providing data scientists and ML engineers with pre-configured example repositories to deploy various ML services, such as SageMaker endpoints or batch transform jobs.
Specific examples of SageMaker Project templates include patterns for LLM training and evaluation, general model building and training, comprehensive model building/training/deployment (supporting real-time, batch, and BYOC), and promoting full ML pipelines across environments. The underlying infrastructure utilizes reusable Terraform modules for AWS services like KMS, Lambda, VPC networking, S3, SageMaker Studio, IAM roles, and Service Catalog.
Deployment involves prerequisites such as preparing multiple AWS accounts, establishing a GitHub organization, and generating a Personal Access Token (PAT). Accounts are bootstrapped with S3 for Terraform state, DynamoDB for state locking, and IAM roles with OIDC for secure GitHub Actions integration. Configuration requires setting GitHub secrets (AWS_ASSUME_ROLE_NAME, PAT_GITHUB) and updating a `config.json` file for multi-environment deployments. While explicit “risks” aren’t detailed, the complexity of managing and securing such a multi-tool, multi-account environment necessitates careful configuration and adherence to security best practices. The article also provides cleanup steps to manage resources and avoid charges.
