This whitepaper is for historical reference only. Some content might be outdated and some links might not be available.
Further reading
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Setting up secure, well-governed machine learning environments on AWS
(AWS blog) -
Configuring Amazon SageMaker AI Studio for teams and groups with complete resource isolation
(AWS blog) -
Onboarding Amazon SageMaker AI Studio with AWS SSO and Okta Universal Directory
(AWS blog) -
How to Configure SAML 2.0 for AWS Account Federation
(Okta documentation) -
Build a Secure Enterprise Machine Learning Platform on AWS
(AWS technical guide) -
Customize Amazon SageMaker AI Studio using Lifecycle Configurations
(AWS blog) -
Bringing your own custom container image to Amazon SageMaker AI Studio notebooks
(AWS blog) -
Build Custom SageMaker AI Project Templates – Best Practices
(AWS blog) -
Multi-account model deployment with Amazon SageMaker AI Pipelines
(AWS blog) -
Part 1: How NatWest Group built a scalable, secure, and sustainable MLOps platform
(AWS blog) -
Secure Amazon SageMaker AI Studio presigned URLs Part 1: Foundational infrastructure
(AWS blog)