Category: Amazon Machine Learning

In this post, we explore how Amazon Bedrock’s multimodal RAG capabilities revolutionize drug data analysis by efficiently processing complex medical documentation containing text, images, graphs, and tables.

In this post, we look at a step-by-step implementation for using the custom document enrichment (CDE) feature within an Amazon Q Business application to process standalone image files. We walk you through an AWS Lambda function configured within CDE to process various image file types, and showcase an example scenario of how this integration enhances Amazon Q Business’s ability to provide comprehensive insights.

In this post, we introduce the new safeguard tiers available in Amazon Bedrock Guardrails, explain their benefits and use cases, and provide guidance on how to implement and evaluate them in your AI applications.

We demonstrate two methods for generating structured responses with Amazon Bedrock: Prompt Engineering and Tool Use with the Converse API. Prompt Engineering is flexible, works with Bedrock models (including those without Tool Use support), and handles various schema types (e.g., Open API schemas), making it a great starting point. Tool Use offers greater reliability, consistent results, seamless API integration, and runtime validation of JSON schema for enhanced control.

In this post, we demonstrate how to build a multi-agent system using multi-agent collaboration in Amazon Bedrock Agents to solve complex business questions in the biopharmaceutical industry. We show how specialized agents in research and development (R&D), legal, and finance domains can work together to provide comprehensive business insights by analyzing data from multiple sources.

In this post, we shared how an internal technology team at Amazon evaluated Amazon Nova models, resulting in notable improvements in inference speed and cost-efficiency.

Today, we’re excited to announce a new integration between Arize AI and Amazon Bedrock Agents that addresses one of the most significant challenges in AI development: observability. In this post, we demonstrate the Arize Phoenix system for tracing and evaluation.

This post is co-written with Sergio Zavota and Amy Perring from NewDay. NewDay has a clear and defining purpose: to help people move forward with credit. NewDay provides around 4 million customers access to credit responsibly and delivers exceptional customer experiences, powered by their in-house technology system. NewDay’s contact center handles 2.5 million calls annually, […]

In this post, we demonstrate how agentic workflow patterns such as Retrieval Augmented Generation (RAG), multi-tool orchestration, and conditional routing with LangGraph enable end-to-end solutions that artificial intelligence and machine learning (AI/ML) developers and enterprise architects can adopt and extend. We walk through an example of a financial management AI assistant that can provide quantitative research and grounded financial advice by analyzing both the earnings call (audio) and the presentation slides (images), along with relevant financial data feeds.

In this post, we present a benchmark of different understanding models from the Amazon Nova family available on Amazon Bedrock, to provide insights on how you can choose the best model for a meeting summarization task.