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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

An AWS account and an AWS Identity and Access Management (IAM) principal with sufficient permissions to create and manage the resources needed for this application. If you don’t have an AWS account, refer to How do I create and activate a new Amazon Web Services account? The script deploys the AWS CDK project in your account.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Digital Transformation in Retail Banks: Potential Impact on Brand Equity, Customers, and Employees

Beyond Philosophy

I’m capitalizing the first letter of each word because the pervasiveness of digital transformation has all the feel of Big Data a few years ago and Reeingineering in the 1990’s. Much of the digital transformation emphasis has been on technology (big data analytics and cloud, mobile apps, etc.)

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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

Data scientists across business units working on model development using Amazon SageMaker are granted access to relevant data, which can lead to the requirement of managing prefix -level access controls. Amazon S3 Access Points simplify managing and securing data access at scale for applications using shared datasets on Amazon S3.

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Accelerating AI/ML development at BMW Group with Amazon SageMaker Studio

AWS Machine Learning

This post is co-written with Marc Neumann, Amor Steinberg and Marinus Krommenhoek from BMW Group. The BMW Group – headquartered in Munich, Germany – is driven by 149,000 employees worldwide and manufactures in over 30 production and assembly facilities across 15 countries.

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Onboard users to Amazon SageMaker Studio with Active Directory group-specific IAM roles

AWS Machine Learning

For provisioning Studio in your AWS account and Region, you first need to create an Amazon SageMaker domain—a construct that encapsulates your ML environment. With SSO mode, you set up an SSO user and group in IAM Identity Center and then grant access to either the SSO group or user from the Studio console.

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