Secure multi-account model deployment with Amazon SageMaker: Part 2

In Part 1 of this series of posts, we offered step-by-step guidance for using Amazon SageMaker, SageMaker projects and Amazon SageMaker Pipelines, and AWS services such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, AWS Key Management Service (AWS KMS), and AWS Identity and Access Management (IAM) to implement secure architectures for multi-account enterprise machine learning (ML) environments. In this second and final part, we provide instructions for deploying the solution from the source code GitHub repository to your account or accounts and experimenting with the delivered SageMaker notebooks. The provided CloudFormation templates provision all the necessary infrastructure and security controls in your account. An Amazon SageMaker Studio domain is also created by the CloudFormation deployment process. The following diagram shows the resources and components that are created in your account.