Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.
Although Studio provides all the tools you need to take your models from experimentation to production, you need a robust and secure model deployment process. This process must fulfill your organization’s operational and security requirements.
Amazon SageMaker and Studio provide a wide range of specialized functionality for building highly secure, scalable, and flexible MLOps platforms to cover your model deployment use cases and requirements. Three SageMaker services, SageMaker Pipelines, SageMaker Projects, and SageMaker Model Registry, build a foundation to implement enterprise-grade secure multi-account model deployment workflow.
In combination with other AWS services, such as Amazon Virtual Private Cloud (Amazon VPC), AWS CloudFormation, and AWS Identity and Access Management (IAM), SageMaker MLOps can deliver solutions for the most demanding security and governance requirements.
Using a multi-account data science environment to meet security, reliability, and operational needs is a good DevOps practice. A multi-account strategy is paramount to achieve strong workload and data isolation, support multiple unrelated teams and projects, ensure fine-grained security and compliance control, facilitate billing, and create cost transparency.
In this two-part post, we offer guidance for using AWS services and SageMaker functionalities, and recommend practices for implementing a production-grade ML platform and secure, automated, multi-account model deployment workflows.
Such ML platforms and workflows can fulfill stringent security requirements, even for regulated industries such as financial services. For example, customers in regulated industries often don’t allow any internet access in ML environments. They often use only VPC endpoints for AWS services. They implement end-to-end data encryption in transit and at rest, and enforce workload isolation for individual teams in a line of business in multi-account organizational structures.
Part 1 of this series focuses on providing a solution architecture overview, in which we explain the security controls employed and how they are implemented. We also look at MLOps automation workflows with SageMaker projects and Pipelines.
In Part 2, we walk through deploying the solution with hands-on SageMaker notebooks.
|This is Part 1 in a two-part series on secure multi-account deployment on Amazon SageMaker
The post Multi-account model deployment with Amazon SageMaker Pipelines shows a concep
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