Introduction to Multi-Account Strategy on AWS
Your multi-account strategy is crucial for creating a secure and scalable environment on AWS. Proper design decisions for your multi-account setup are essential for efficient operations at scale. Grouping workloads strategically into different AWS accounts allows for better control, cost monitoring, and reduced complexity in managing multiple VPCs and identities.
Implementing a Multi-Account Foundation Architecture
In this article, we provide guidance on building a multi-account foundation architecture for organizing, building, and governing various modules including data lake foundations, ML platform services, ML use case development, and more. This architecture helps in governing the ML lifecycle at scale and fosters innovation while ensuring compliance with organizational policies.
Organizing Your AWS Environment with AWS Organizations
Utilizing AWS Organizations enables you to centrally manage accounts in your AWS environment. Through hierarchical groupings of accounts within organizational units, you can efficiently organize and manage your multi-account setup. The recommended OU structure for your data and ML environment is based on best practices outlined in the AWS whitepaper.
Building a Secure Architecture with AWS Control Tower
AWS Control Tower plays a vital role in initializing a well-architected multi-account environment with security and compliance best practices. By setting up AWS Control Tower and creating the necessary accounts and OUs, you can establish a strong foundation for your ML platform. The tool helps automate tasks and configurations to streamline operations.
Access Management and Governance with IAM Identity Center
IAM Identity Center provides a framework for creating fine-grained access control for different user groups within your ML platform. By organizing users into platform-wide and team-specific groups, you can ensure proper access management and governance. Utilizing IAM Identity Center groups helps in maintaining security and compliance.
Scaling ML Workloads with AWS Service Catalog
AWS Service Catalog allows IT administrators to create, manage, and distribute portfolios of approved products for end-users. By leveraging AWS Service Catalog portfolios and products, you can enhance and scale capabilities within your AWS environment. Implementing AWS Service Catalog with best practices supports governance and scalability for ML workloads.
Network Infrastructure and Connectivity
Establishing scalable network architecture using services like Amazon VPC and AWS Transit Gateway is crucial as your environment grows. Creating hub and spoke VPC architecture with the right connectivity for production and development environments ensures secure and compliant network setup. This approach facilitates seamless connectivity for different workloads.
Conclusion
In this overview of best practices for a multi-account foundation on AWS, we have discussed key components such as AWS Organizations, AWS Control Tower, IAM Identity Center, AWS Service Catalog, and network architecture considerations. By following these guidelines and utilizing the recommended tools, you can establish a robust environment for governing your analytics and ML workloads effectively at scale. Subscribe to the AWS Machine Learning Blog for more insights in this series.
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