Implementing a Robust MLOps Platform with Terraform and GitHub Actions
Introduction to MLOps
Understanding the Role of Machine Learning Operations in Production
Solution Overview
Building a Comprehensive MLOps Architecture
Custom SageMaker Project Templates
Pre-Built Templates for Efficient ML Deployment
Infrastructure Terraform Modules
Reusable Components for Automated Deployments
Prerequisites
Getting Started with Your MLOps Deployment
Bootstrapping Your AWS Accounts
Preparing Infrastructure for GitHub and Terraform Integration
Bootstrap Using a CloudFormation Template
Bootstrap Using a Bash Script
Setting Up Your GitHub Organization
Structuring Repositories for Effective Collaboration
Base Infrastructure
Template Repositories
Updating the Configuration File
Configuring Environments for Multi-Account Management
Deploying the Infrastructure
Launching Your MLOps Platform
End-User Experience
Navigating the MLOps Workflow
Clean Up
Safeguarding Resources After Use
Conclusion
Recap of Deploying a Modular MLOps Platform
About the Authors
Meet Your Experts in MLOps and AI/ML Solutions
Building an MLOps Platform with Terraform, GitHub, and SageMaker: A Comprehensive Guide
In the fast-evolving domain of machine learning (ML), ensuring the efficient deployment and management of models is paramount. This is where Machine Learning Operations (MLOps) shines. MLOps combines people, processes, and technology to streamline ML use cases, advocating for reproducibility, robustness, and observability throughout the lifecycle of ML models. In this post, we’ll explore how to construct a robust MLOps platform using Terraform, GitHub, and SageMaker.
Why MLOps Matters
An effective MLOps platform serves as the backbone for enterprises, necessitating a multi-account strategy with stringent security protocols. Ideal implementations use continuous integration and delivery (CI/CD) practices while restricting user interaction to managed code repositories. For an in-depth understanding of MLOps best practices, consider consulting the MLOps Foundation roadmap for enterprises leveraging Amazon SageMaker.
The Role of Terraform and GitHub
Terraform by HashiCorp has gained popularity as the predominant approach for infrastructure as code (IaC), allowing developers to establish and modify AWS infrastructure seamlessly. Coupled with GitHub for version control and GitHub Actions for CI/CD, these tools have become cornerstones of the DevOps and MLOps communities.
Solution Overview
Our MLOps architecture enables a systematic approach to ML operations by establishing a comprehensive infrastructure that includes:
- Model Training Pipeline: Setting up a pipeline for training and optimizing models.
- Model Registry: Utilizing Amazon SageMaker Model Registry for model versioning and tracking.
- Environment Management: Managing both preproduction and production environments.
Together, these elements foster an organized framework that enhances the transition from model development to deployment.
Custom SageMaker Project Templates
SageMaker Projects facilitate the setup of standardized environments for data scientists and MLOps engineers. Upon selecting a project template, a GitHub repository is automatically created, equipping users with the necessary CI/CD resources tailored to their needs.
Currently, we offer four custom SageMaker Project templates:
- LLM Training and Evaluation: A template for training large language models (LLMs).
- Model Building and Training: A simplistic setup for model training and evaluation.
- Building, Training, and Deployment: A comprehensive solution for real-time and batch inference.
- Promoting Full ML Pipeline Across Environments: A template focused on maintaining consistency in ML pipelines from development through production.
Each template comes with preconfigured GitHub repositories that data scientists can clone and customize.
Infrastructure Code with Terraform
The Terraform infrastructure modules are organized to promote reusability across various environments. Key elements include:
- Standardized modules found in the
base-infrastructure/terraformdirectory. - Environment-specific configurations to ensure deployment consistency.
Prerequisites
Before diving into the deployment process, ensure the following:
- AWS Accounts: Set up three AWS accounts for experimentation, preproduction, and production.
- GitHub Organization: Create a GitHub organization to host your repositories.
- Personal Access Token (PAT): Generate a PAT with the necessary permissions for your setup.
Bootstrapping AWS Accounts for GitHub and Terraform
Bootstrapping your AWS accounts is crucial for maintaining resource state and enabling GitHub to deploy resources efficiently. You have two options for bootstrapping:
-
CloudFormation Template:
- Utilize the AWS CLI to create a CloudFormation stack.
-
Bash Script:
- Execute a provided script to bootstrap resources easily.
Configuring Your GitHub Organization
Set up your GitHub organization by cloning the example code into specific repositories. This involves:
- Creating a base infrastructure repository for Terraform code.
- Setting up GitHub Actions for CI/CD workflows.
- Adding secrets to your repository, such as your AWS role name and GitHub PAT.
Deploying the Infrastructure
With the organization and resources set up, you’re ready to deploy to AWS accounts. This can be triggered when changes are made to the main branch of your repository or initiated manually via the GitHub Actions tab.
End-User Experience
Once the infrastructure is deployed, data scientists and ML engineers can interact with the platform, customizing their workflows and resources as required.
Cleanup
To avoid unnecessary charges, resources created during testing and development should be cleaned up. This involves deleting SageMaker artifacts, Git repositories, and AWS resources in a systematic manner.
Conclusion
In this post, we’ve illustrated the foundational steps for deploying an MLOps platform using Terraform, GitHub, and Amazon SageMaker. By integrating custom SageMaker Project templates and leveraging efficient CI/CD workflows, organizations can streamline their ML efforts significantly.
For more implementation details and source code, visit the GitHub repository.
About the Authors
Jordan Grubb is a DevOps Architect at AWS, focusing on MLOps to deliver automated cloud architectures.
Irene Arroyo Delgado is an AI/ML and GenAI Specialist Solutions Architect at AWS, dedicated to enhancing the potential of generative AI and ML workloads.
By utilizing advanced tools like Terraform and GitHub together with Amazon SageMaker, businesses can enhance their capacity to deploy, manage, and innovate within the machine learning space efficiently.