Category: Artificial Intelligence

The Amazon Finance technical team develops and manages comprehensive technology solutions that power financial decision-making and operational efficiency while standardizing across Amazon’s global operations. In this post, we explain how the team conceptualized and implemented a solution to these business challenges by harnessing the power of generative AI using Amazon Bedrock and intelligent search with Amazon Kendra.

In this post, we announce that Mercury and Mercury Coder foundation models from Inception Labs are now available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. We demonstrate how to deploy these ultra-fast diffusion-based language models that can generate up to 1,100 tokens per second on NVIDIA H100 GPUs, and showcase their capabilities in code generation and tool use scenarios.

In this post, we show you how Amazon Health Services (AHS) solved discoverability challenges on Amazon.com search using AWS services such as Amazon SageMaker, Amazon Bedrock, and Amazon EMR. By combining machine learning (ML), natural language processing, and vector search capabilities, we improved our ability to connect customers with relevant healthcare offerings.

Applying emerging technologies to the geospatial domain offers a unique opportunity to create transformative user experiences and intuitive workstreams for users and organizations to deliver on their missions and responsibilities. In this post, we explore how you can integrate existing systems with Amazon Bedrock to create new workflows to unlock efficiencies insights. This integration can benefit technical, nontechnical, and leadership roles alike.

As the model landscape expands, organizations face complex scenarios when selecting the right foundation model for their applications. In this blog post we present a systematic evaluation methodology for Amazon Bedrock users, combining theoretical frameworks with practical implementation strategies that empower data scientists and machine learning (ML) engineers to make optimal model selections.

In this post, we introduce our open source GenAI IDP Accelerator—a tested solution that we use to help customers across industries address their document processing challenges. Automated document processing workflows accurately extract structured information from documents, reducing manual effort. We will show you how this ready-to-deploy solution can help you build those workflows with generative AI on AWS in days instead of months.

In this post, we introduced three features in SageMaker HyperPod that enhance scalability and customizability for ML infrastructure. Continuous provisioning offers flexible resource provisioning to help you start training and deploying your models faster and manage your cluster more efficiently. With custom AMIs, you can align your ML environments with organizational security standards and software requirements.

This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so you can spin up high-performance clusters on demand. In this post, […]

We are excited to announce the public preview of support for inline code nodes in Amazon Bedrock Flows. With this powerful new capability, you can write Python scripts directly within your workflow, alleviating the need for separate AWS Lambda functions for simple logic. This feature streamlines preprocessing and postprocessing tasks (like data normalization and response formatting), simplifying generative AI application development and making it more accessible across organizations.

Amazon Q Business offers AWS customers a scalable and comprehensive solution for enhancing business processes across their organization. By carefully evaluating your use cases, following implementation best practices, and using the architectural guidance provided in this post, you can deploy Amazon Q Business to transform your enterprise productivity. The key to success lies in starting small, proving value quickly, and scaling systematically across your organization.