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As ML teams grow, embedding Amazon SageMaker AI MLflow Apps into a custom portal requires a scalable approach to access management. Distributing presigned URLs doesn’t scale for teams with dozens of data scientists, and granting individual AWS Management Console access adds operational overhead for administrators managing access controls. Teams who rely on SSO-integrated internal portals need their MLflow experiment tracking accessible alongside other internal applications through a single bookmarkable URL. With a custom portal, you reduce onboarding time for new team members, simplify access management, and give data scientists a consistent experience across your internal tools.…

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