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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 97
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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.

APIs 121
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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

Building on the concept of dynamically fetching up-to-date data to produce personalized content, the use of LLMs has garnered significant attention in recent research for recommender systems. In summary, intelligent agents could construct prompts using user- and item-related data and deliver customized natural language responses to users.

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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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Add conversational AI to any contact center with Amazon Lex and the Amazon Chime SDK

AWS Machine Learning

Reviewing the Account Balance chatbot. As an example, this demo deploys a bot to perform three automated tasks, or intents : Check Balance , Transfer Funds , and Open Account. For example, the Open Account intent includes four slots: First Name. Account Type. Complete the following steps: Log in to your AWS account.

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How Twilio generated SQL using Looker Modeling Language data with Amazon Bedrock

AWS Machine Learning

Prerequisites To implement the solution, you should have an AWS account , model access to your choice of FM on Amazon Bedrock, and familiarity with DynamoDB, Amazon RDS, and Amazon S3. After access is provided to a model, it is available for the users in the account. Access to Amazon Bedrock FMs isn’t granted by default.