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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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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.

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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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10 Customer Experience Best Practices for all B2B Sales Teams

Kayako

In this article, we take a closer look at ten of the customer experience best practices that all B2B sales teams should adopt, in order to stay competitive, meet or exceed expectations, and build loyalty. . Make Customer Experience a Priority . Define the Ideal Customer Experience . Create a Customer Service Coaching Plan .

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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.