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Build a scalable AI assistant to help refugees using AWS

AWS Machine Learning Blog

This post details our technical implementation using AWS services to create a scalable, multilingual AI assistant system that provides automated assistance while maintaining data security and GDPR compliance. Amazon Titan Embeddings also integrates smoothly with AWS, simplifying tasks like indexing, search, and retrieval.

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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning Blog

SageMaker Unified Studio combines various AWS services, including Amazon Bedrock , Amazon SageMaker , Amazon Redshift , Amazon Glue , Amazon Athena , and Amazon Managed Workflows for Apache Airflow (MWAA) , into a comprehensive data and AI development platform. Navigate to the AWS Secrets Manager console and find the secret -api-keys.

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Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. The example extracts and contextualizes the buildspec-1-10-2.yml

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). To address customers’ requirements about data privacy and sovereignty, SnapLogic deploys the data plane within the customer’s VPC on AWS.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. In the first post , we described FL concepts and the FedML framework.

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How SnapLogic built a text-to-pipeline application with Amazon Bedrock to translate business intent into action

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In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

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Customizing coding companions for organizations

AWS Machine Learning Blog

In these two studies, commissioned by AWS, developers were asked to create a medical software application in Java that required use of their internal libraries. About the authors Qing Sun is a Senior Applied Scientist in AWS AI Labs and work on AWS CodeWhisperer, a generative AI-powered coding assistant.

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