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Machine learning

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Machine learning - Amazon SageMaker Unified Studio
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Amazon SageMaker Unified Studio is a unified development experience for building analytics, AI/ML, and generative AI applications at scale. This chapter describes the Amazon SageMaker AI capabilities that you can use in Amazon SageMaker Unified Studio.

Note

When you add a custom tag to a SageMaker AI resource (such as a training job, inference endpoint, model, or pipeline), add the prefix ProjectUserTag to the tag name. For example:

ProjectUserTagMyCustomTag
Note

ECR repositories must be created with the AmazonDataZoneProject tag with the project ID (which can be found under project details in the project overview page or from the page URL) as the tag value. If you want to add your own tags, they must be prefixed with ProjectUserTag.

For example, with AWS CLI:

aws ecr create-repository \ --repository-name my-repo \ --tags \ Key=AmazonDataZoneProject,Value=5blxelum5cmckb \ Key=ProjectUserTagMyTag,Value=MyTagValue \

Example using Jupyterlab notebook:

import boto3 # Create ECR client ecr_client = boto3.client('ecr') # Define repository name repository_name = 'my-ecr-repo' # Define tags tags = [ { 'Key': 'AmazonDataZoneProject', 'Value': '5blxelum5cmckb' }, { 'Key': 'ProjectUserTagMyTag', 'Value': 'MyTagValue' }, ] try: # Create the repository with tags response = ecr_client.create_repository( repositoryName=repository_name, imageScanningConfiguration={ 'scanOnPush': True }, encryptionConfiguration={ 'encryptionType': 'AES256' }, tags=tags ) repository_uri = response['repository']['repositoryUri'] print(f"Repository created successfully!") print(f"Repository URI: {repository_uri}") except ecr_client.exceptions.RepositoryAlreadyExistsException: print(f"Repository {repository_name} already exists") # Add tags to existing repository ecr_client.tag_resource( resourceArn=f"arn:aws:ecr:{ecr_client.meta.region_name}:{boto3.client('sts').get_caller_identity()['Account']}:repository/{repository_name}", tags=tags ) # Get the repository URI response = ecr_client.describe_repositories(repositoryNames=[repository_name]) repository_uri = response['repositories'][0]['repositoryUri'] print(f"Added tags to existing repository") print(f"Repository URI: {repository_uri}") except Exception as e: print(f"Error creating repository: {str(e)}")

ECR repositories without the AmazonDataZoneProject cannot be used. You must create new ECR repositories with the AmazonDataZoneProject tag. Once tagged with the AmazonDataZoneProject tag, this tag cannot be modified or removed from your ECR repositories. For more information about ECR repositories, see https://docs.aws.amazon.com/AmazonECR/latest/userguide/what-is-ecr.html.

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