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On Being an Accountable Customer Service Leader

Customer Service Life

Properly authenticating the account. Leaving complete account notes for the next person who interacts with the customer. This exercise reminded me of the time when we started this blog back in 2012. Starting a blog about customer service became instant accountability for me. Quality as accountability.

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Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points

AWS Machine Learning

With an increase in use cases and datasets using bucket policy statements, managing cross-account access per application is too complex and long for a bucket policy to accommodate. This post walks through the steps involved in configuring S3 Access Points to enable cross-account access from a SageMaker notebook instance.

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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

The Amazon Bedrock VPC endpoint powered by AWS PrivateLink allows you to establish a private connection between the VPC in your account and the Amazon Bedrock service account. Use the following template to create the infrastructure stack Bedrock-GenAI-Stack in your AWS account. You’re redirected to the IAM console.

APIs 139
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Manage Amazon SageMaker JumpStart foundation model access with private hubs

AWS Machine Learning

Finally, admins can share access to private hubs across multiple AWS accounts, enabling collaborative model management while maintaining centralized control. SageMaker JumpStart uses AWS Resource Access Manager (AWS RAM) to securely share private hubs with other accounts in the same organization.

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Detect and protect sensitive data with Amazon Lex and Amazon CloudWatch Logs

AWS Machine Learning

For example, you may have the following data types: Name Address Phone number Email address Account number Email address and physical mailing address are often considered a medium classification level. These policies allow to audit and mask sensitive data that appears in log events ingested by the log groups in your account.