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This post is co-authored by Manuel Lopez Roldan, SiMa.ai, and Jason Westra, AWS Senior Solutions Architect. Are you looking to deploy machine learning (ML) models at the edge? With Amazon SageMaker AI and SiMa.ai’s Palette Edgematic platform, you can efficiently build, train, and deploy optimized ML models at the edge for a variety of use …

Accelerate edge AI development with SiMa.ai Edgematic with a seamless AWS integration Read More »

This post is co-written with Ken Tsui, Edward Tsoi and Mickey Yip from Apoidea Group. The banking industry has long struggled with the inefficiencies associated with repetitive processes such as information extraction, document review, and auditing. These tasks, which require significant human resources, slow down critical operations such as Know Your Customer (KYC) procedures, loan …

How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod Read More »

This post is co-authored by Jay Kshirsagar and Ronald Quan from Qualtrics. The content and opinions in this post are those of the third-party author and AWS is not responsible for the content or accuracy of this post. Qualtrics, founded in 2002, is a pioneering software company that has spent over two decades creating exceptional …

How Qualtrics built Socrates: An AI platform powered by Amazon SageMaker and Amazon Bedrock Read More »

Vxceed delivers SaaS solutions across industries such as consumer packaged goods (CPG), transportation, and logistics. Its modular environments include Lighthouse for CPG demand and supply chains, GroundCentric247 for airline and airport operations, and LimoConnect247 and FleetConnect247 for passenger transport. These solutions support a wide range of customers, including government agencies in Australia and New Zealand. …

Vxceed secures transport operations with Amazon Bedrock Read More »

In honor of Global Accessibility Awareness Day, we’re excited to roll out new updates across Android and Chrome, plus new resources for the ecosystem. ​ 

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 AWS Inferentia are purpose-built AI chips to accelerate machine learning (ML) workloads, making them ideal for …

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

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 recipes. In this post, we use the recipes to fine-tune the original DeepSeek-R1 671b parameter model. …

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

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 operational assistance. In this post, we show you how Amazon Q Business can help augment your …

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

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. In this post, we explore the security measures and practical strategies provided by Amazon Bedrock Agents …

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

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 prompt to deliver more context-specific and accurate responses without continuously retraining the FM, while also improving …

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