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Scaling machine learning (ML) workflows from initial prototypes to large-scale production deployment can be daunting task, but the integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution to this challenge. ML SA), Monidipa Chakraborty (Sr. Delete the IAM role you created.
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jpg", "prompt": "Which part of Virginia is this letter sent from", "completion": "Richmond"} SageMaker JumpStart SageMaker JumpStart is a powerful feature within the SageMaker machine learning (ML) environment that provides ML practitioners a comprehensive hub of publicly available and proprietary foundation models (FMs).
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Create a role named sm-build-role with the following trust policy, and add the policy sm-build-policy that you created earlier: { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "Service": "codebuild.amazonaws.com" }, "Action": "sts:AssumeRole" } ] } Now, let’s review the steps in CloudShell. base-ubuntu18.04
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