
Amazon SageMaker Canvas now allows users to specify custom Amazon S3 output locations for ML artifacts, enabling better organization and control over trained models, reports, and predictions.


<div><p>This article announces that Amazon SageMaker Canvas now supports custom Amazon S3 output locations for ML artifacts, giving users greater control over where their machine learning outputs are stored and organized.</p><ul><li>Users can specify custom S3 locations for trained models, explainability reports, and predictions</li><li>Enables organization of outputs by user, team, or company conventions</li><li>Simplifies sharing and accessing ML artifacts with colleagues and collaborators</li><li>Allows automation of artifact distribution and deployment to specific platforms</li><li>Previously, SageMaker Canvas used pre-created S3 locations that couldn't be customized</li><li>Feature available in all AWS regions supporting SageMaker Canvas</li></ul><p>This enhancement gives business analysts and citizen data scientists greater flexibility in managing their ML experiment outputs without requiring coding expertise.</p></div>


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