aws-ai-landing-zone

aws-ai-landing-zone

Reference architecture for https://www.linkedin.com/pulse/ai-aws-landing-zone-secure-architecture-blueprint-era-phillip-bailey-foije/

Overview

The AWS AI Landing Zone is a well-architected, scalable framework for organizing and managing AI/ML workloads on AWS. It provides a structured approach to set up AWS accounts, security controls, networking, and AI/ML services across development, testing, and production environments.

Purpose

This landing zone helps organizations:

• Establish a secure foundation for AI/ML workloads
• Implement proper isolation between environments
• Apply consistent security and governance controls
• Optimize costs through FinOps practices
• Enable observability and monitoring
• Streamline the MLOps lifecycle

Architecture

The landing zone consists of several organizational units (OUs):

Management OU

Central account for managing the entire landing zone with services like:

• AWS Config
• IAM Identity Center
• AWS KMS
• AWS Organizations

Security OU

Dedicated to security monitoring and enforcement:

• GuardDuty
• Security Hub
• Macie
• Network monitoring

Observability OU

Centralized monitoring and observability:

• CloudWatch
• X-Ray
• Elasticsearch/OpenSearch

FinOps OU

Cost management and optimization:

• Budgets
• Tagging strategies
• Cost Explorer
• Optimization tools

AI Environment OUs

Separate OUs for different stages of the AI/ML lifecycle:

AI DEV OU

Development environment with:

• MLOps & Automation Account
• Network Account
• Data processing pipeline:
  • Dirty Datasets Account
  • Data Transformation Account
  • Processed Datasets Account
• LLMs and Foundation Models Account
• Application Account
• Frontend Account

AI TEST OU

Similar structure to DEV but isolated for testing purposes

AI PROD OU

Production environment with the highest security and reliability standards

Complementary Services

The landing zone integrates with AWS AI services:

• AWS Lake Formation
• AWS Glue
• AWS Step Function
• AWS Secrets Manager
• AWS API Gateway
• AWS SageMaker
• AWS Bedrock

Getting Started

1. Begin with the Management Account setup
2. Deploy the Security OU and establish baseline security controls
3. Configure the Observability OU for monitoring
4. Set up the FinOps OU for cost management
5. Deploy the AI environment OUs based on your development lifecycle needs

Best Practices

• Maintain strict separation between DEV, TEST, and PROD environments
• Apply least privilege access controls
• Implement consistent tagging for resources
• Set up automated CI/CD pipelines for MLOps
• Establish data governance policies
• Monitor costs and performance regularly

Support

For questions or support feel free to reach out.

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Created by Phillip Bailey | Version 1.04082022