Source: aws.amazon.com
New ML Governance Tools for Amazon SageMaker – Simplify Access Control and
https://aws.amazon.com/polly/ As companies increasingly adopt machine learning (ML) for their business applications, they are looking for ways to improve governance of their ML projects with simplified access control and enhanced visibility across the ML lifecycle. Another challenge is improving visibility over ML projects.
As an ML system or platform administrator, you can now use Amazon SageMaker Role Manager to define custom permissions for SageMaker users in minutes, so you can onboard users faster.
Introducing Amazon SageMaker Role Manager SageMaker Role Manager lets you define custom permissions for SageMaker users in minutes.
You can select the Data Scientist, MLOps, or Custom persona in SageMaker Role Manager to start creating service roles with custom permissions for your ML practitioners.
As an ML system or platform administrator, you can now use Amazon SageMaker Role Manager to define custom permissions for SageMaker users in minutes, so you can onboard users faster.
Introducing Amazon SageMaker Role Manager SageMaker Role Manager lets you define custom permissions for SageMaker users in minutes.
You can select the Data Scientist, MLOps, or Custom persona in SageMaker Role Manager to start creating service roles with custom permissions for your ML practitioners.
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