Here’s the translation into American English:
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Managing access control in enterprise machine learning environments presents a complex challenge, especially when multiple teams share resources in Amazon SageMaker under a single Amazon Web Services (AWS) account. Although Amazon SageMaker Studio provides tools to assign user-level execution roles, the situation becomes more intricate as organizations expand and their teams grow.
To address this challenge, it’s essential to implement permission management strategies that focus on attribute-based access control (ABAC) patterns. These patterns allow for more granular access control while minimizing the proliferation of AWS Identity and Access Management (IAM) roles.
In regulated sectors, such as finance or healthcare, a machine learning (ML) platform team may manage a comprehensive infrastructure that serves multiple data science groups. This centralized structure facilitates the application of consistent governance policies. However, challenges include maintaining workload isolation between teams and managing permissions among users within the same team.
To preserve resource separation, platform teams can establish dedicated Amazon SageMaker Studio domains for each business unit. Currently, methods are emerging to implement attribute-based access control that leverages IAM policy variables, allowing user-level controls without sacrificing security, while maintaining domain-level execution roles.
Key elements of this solution include source identity and context keys. The source identity is a custom string that administrators can use during role assumption, identifying the user or application executing certain actions. This information is logged in AWS CloudTrail and persists through role chaining.
For effective access control in scenarios where multiple users share a SageMaker Studio domain, administrators must establish resource-level access controls. This ensures that data scientists cannot accidentally delete their peers’ resources. Context keys such as sagemaker:DomainId and sagemaker:UserProfileName are powerful tools for administrators looking to create dynamic ABAC policies.
By incorporating these best practices in access management, organizations can optimize resource utilization, ensure compliance with security regulations, and enhance operational efficiency in their ML workflows. This underscores the importance of auditing user access through detailed logs, providing visibility into who accessed which resources and when, thereby improving security and regulatory compliance.
In summary, effective strategies for implementing user-level access control in SageMaker Studio and other AWS platforms are presented. By combining SageMaker AI resources, context keys, and the propagation of source identities, organizations can develop dynamic policies that automatically scale permissions based on user identity while maintaining shared execution roles.
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Source: MiMub in Spanish
