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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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Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.

APIs 82
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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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More Than Just Number-Crunchers: How Accountants Provide Value-Added Services

Method:CRM

Those poor accountants. In fact, today’s accountants are far more than just number-crunchers — they’re leaders, strategists, technologists, advisors and business specialists. The accounting industry: (p)art of the deal. Accountants speak the language of business. For instance, look at large accounting organizations.

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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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Define customized permissions in minutes with Amazon SageMaker Role Manager via the AWS CDK

AWS Machine Learning

with the following code: import * as cdk from 'aws-cdk-lib'; import { Construct } from 'constructs'; import * as iam from 'aws-cdk-lib/aws-iam'; import { Activity } from '@cdklabs/cdk-aws-sagemaker-role-manager'; export class RoleManagerStack extends cdk.Stack { constructor(scope: Construct, id: string, props?

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning

in your editor and modify the STACK_NAME and CUSTOM_HEADER_VALUE variables: The stack name enables you to deploy multiple applications in the same account. About the Author Lior Perez is a Principal Solutions Architect on the Construction team based in Toulouse, France. Open config_file.py