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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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Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering

AWS Machine Learning

As described in the AWS Well-Architected Framework , separating workloads across accounts enables your organization to set common guardrails while isolating environments. Organizations with a multi-account architecture typically have Amazon Redshift and SageMaker Studio in two separate AWS accounts.

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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.

Scripts 124
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Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning

Provide the AWS Region, account, and model IDs appropriate for your environment. Lijan Kuniyil is a Senior Technical Account Manager at AWS. Limit access to all Amazon Bedrock models To restrict access to all Amazon Bedrock models, you can modify the SageMaker role to explicitly deny these APIs.

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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 140