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Customize DeepSeek-R1 671b model using Amazon SageMaker HyperPod recipes – Part 2

This post is the second part of the DeepSeek series focusing on model customization with Amazon SageMaker HyperPod recipes (or recipes for brevity). In Part 1, we demonstrated the performance and ease of fine-tuning DeepSeek-R1 distilled models using these...

Cost-effective AI image generation with PixArt-Σ inference on AWS Trainium and AWS Inferentia

PixArt-Sigma is a diffusion transformer model that is capable of image generation at 4k resolution. This model shows significant improvements over previous generation PixArt models like Pixart-Alpha and other diffusion models through dataset and architectural improvements. AWS Trainium and...

Build a financial research assistant using Amazon Q Business and Amazon QuickSight for generative AI–powered insights

According to a Gartner survey in 2024, 58% of finance functions have adopted generative AI, marking a significant rise in adoption. Among these, four primary use cases have emerged as especially prominent: intelligent process automation, anomaly detection, analytics, and...

Securing Amazon Bedrock Agents: A guide to safeguarding against indirect prompt injections

Generative AI tools have transformed how we work, create, and process information. At Amazon Web Services (AWS), security is our top priority. Therefore, Amazon Bedrock provides comprehensive security controls and best practices to help protect your applications and data....

How Hexagon built an AI assistant using AWS generative AI services

This post was co-written with Julio P. Roque Hexagon ALI. Recognizing the transformative benefits of generative AI for enterprises, we at Hexagon’s Asset Lifecycle Intelligence division sought to enhance how users interact with our Enterprise Asset Management (EAM) products. Understanding...

Build scalable containerized RAG based generative AI applications in AWS using Amazon EKS with Amazon Bedrock

Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. This data is used to enrich the generative AI...
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