Category: Advanced (300)

In this post, we demonstrate how to use the evaluation features of Amazon Bedrock to deliver reliable results across language barriers without the need for localized prompts or custom infrastructure. Through comprehensive testing and analysis, we share practical strategies to help reduce the cost and complexity of multilingual evaluation while maintaining high standards across global large language model (LLM) deployments.

Today, we are excited to announce that Qwen3, the latest generation of large language models (LLMs) in the Qwen family, is available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Qwen3 models—available in 0.6B, 4B, 8B, and 32B parameter sizes—to build, experiment, and responsibly scale your generative AI applications on AWS. In this post, we demonstrate how to get started with Qwen3 on Amazon Bedrock Marketplace and SageMaker JumpStart.

In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using SageMaker AI inference for real-time inference. We also discuss best practices to choose the right instance size and share some debugging best practices while working with JupyterLab notebooks in SageMaker Unified Studio.

This post provides instructions to configure a structured data retrieval solution, with practical code examples and templates. It covers implementation samples and additional considerations, empowering you to quickly build and scale your conversational data interfaces.

In the context of distributed systems and microservices architecture, orchestrating communication between diverse components presents significant challenges. However, with the launch of Amazon Bedrock Agents, the landscape is evolving, offering a simplified approach to agent creation and seamless integration of the return of control capability. In this post, we explore how Amazon Bedrock Agents revolutionizes agent creation and demonstrates the efficacy of the return of control capability in orchestrating complex interactions between multiple systems.

The Institute of Science Tokyo has successfully trained Llama 3.3 Swallow, a 70-billion-parameter large language model (LLM) with enhanced Japanese capabilities, using Amazon SageMaker HyperPod. The model demonstrates superior performance in Japanese language tasks, outperforming GPT-4o-mini and other leading models. This technical report details the training infrastructure, optimizations, and best practices developed during the project.

Gardenia Technologies, a data analytics company, partnered with the AWS Prototyping and Cloud Engineering (PACE) team to develop Report GenAI, a fully automated ESG reporting solution powered by the latest generative AI models on Amazon Bedrock. This post dives deep into the technology behind an agentic search solution using tooling with Retrieval Augmented Generation (RAG) and text-to-SQL capabilities to help customers reduce ESG reporting time by up to 75%. We demonstrate how AWS serverless technology, combined with agents in Amazon Bedrock, are used to build scalable and highly flexible agent-based document assistant applications.

When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using Amazon Bedrock to identify relationships between regulation changes and SOPs.

In this post, we explore how a leading GeoFM (Clay Foundation’s Clay foundation model available on Hugging Face) can be deployed for large-scale inference and fine-tuning on Amazon SageMaker.

In this post, we demonstrate an example of building an agentic RAG application using the LlamaIndex framework. LlamaIndex is a framework that connects FMs with external data sources. It helps ingest, structure, and retrieve information from databases, APIs, PDFs, and more, enabling the agent and RAG for AI applications. This application serves as a research tool, using the Mistral Large 2 FM on Amazon Bedrock generate responses for the agent flow.