Category: Compute

This post explores deploying a text-to-SQL pipeline using generative AI models and Amazon Bedrock to ask natural language questions to a genomics database. We demonstrate how to implement an AI assistant web interface with AWS Amplify and explain the prompt engineering strategies adopted to generate the SQL queries. Finally, we present instructions to deploy the service in your own AWS account.

Rufus, an AI-powered shopping assistant, relies on many components to deliver its customer experience including a foundation LLM (for response generation) and a query planner (QP) model for query classification and retrieval enhancement. This post focuses on how the QP model used draft centric speculative decoding (SD)—also called parallel decoding—with AWS AI chips to meet the demands of Prime Day. By combining parallel decoding with AWS Trainium and Inferentia chips, Rufus achieved two times faster response times, a 50% reduction in inference costs, and seamless scalability during peak traffic.

In this post, we present a solution to incorporate Amazon Bedrock Agents in your Slack workspace. We guide you through configuring a Slack workspace, deploying integration components in Amazon Web Services, and using this solution.

In this post, we demonstrate a solution using Amazon Elastic Kubernetes Service (EKS) with Amazon Bedrock to build scalable and containerized RAG solutions for your generative AI applications on AWS while bringing your unstructured user file data to Amazon Bedrock in a straightforward, fast, and secure way.

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 these advantages, we partnered with AWS to embark on a journey to develop HxGN Alix, an AI-powered digital worker using AWS generative AI services. This blog post explores the strategy, development, and implementation of HxGN Alix, demonstrating how a tailored AI solution can drive efficiency and enhance user satisfaction.

In this post, we showcase how Dr. Kori Ramajoo, Dr. Sonia Brownsett, Prof. David Copland, from QARC, and Scott Harding, a person living with aphasia, used AWS services to develop WordFinder, a mobile, cloud-based solution that helps individuals with aphasia increase their independence through the use of AWS generative AI technology.

In this post, we use the multi-agent feature of Amazon Bedrock to demonstrate a powerful and innovative approach to AWS cost management. By using the advanced capabilities of Amazon Nova FMs, we’ve developed a solution that showcases how AI-driven agents can revolutionize the way organizations analyze, optimize, and manage their AWS costs.

In this post, we demonstrate how to orchestrate multiple Amazon Bedrock agents to create a sophisticated Amazon EKS troubleshooting system. By enabling collaboration between specialized agents—deriving insights from K8sGPT and performing actions through the ArgoCD framework—you can build a comprehensive automation that identifies, analyzes, and resolves cluster issues with minimal human intervention.

In this post, we explore how Low-Rank Adaptation (LoRA) can be used to address these challenges effectively. Specifically, we discuss using LoRA serving with LoRA eXchange (LoRAX) and Amazon Elastic Compute Cloud (Amazon EC2) GPU instances, allowing organizations to efficiently manage and serve their growing portfolio of fine-tuned models, optimize costs, and provide seamless performance for their customers.

In this post, we present a solution using generative AI and large language models (LLMs) to alleviate the time-consuming and labor-intensive tasks required to build a computer vision application, enabling you to immediately start taking pictures of your asset labels and extract the necessary information to update the inventory using AWS services