Artificial Intelligence
Category: Intermediate (200)
Medical reports analysis dashboard using Amazon Bedrock, LangChain, and Streamlit
In this post, we demonstrate the development of a conceptual Medical Reports Analysis Dashboard that combines Amazon Bedrock AI capabilities, LangChain’s document processing, and Streamlit’s interactive visualization features. The solution transforms complex medical data into accessible insights through a context-aware chat system powered by large language models available through Amazon Bedrock and dynamic visualizations of health parameters.
Migrate from Anthropic’s Claude 3.5 Sonnet to Claude 4 Sonnet on Amazon Bedrock
This post provides a systematic approach to migrating from Anthropic’s Claude 3.5 Sonnet to Claude 4 Sonnet on Amazon Bedrock. We examine the key model differences, highlight essential migration considerations, and deliver proven best practices to transform this necessary transition into a strategic advantage that drives measurable value for your organization.
Build trustworthy AI agents with Amazon Bedrock AgentCore Observability
In this post, we walk you through implementation options for both agents hosted on Amazon Bedrock AgentCore Runtime and agents hosted on other services like Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Kubernetes Service (Amazon EKS), AWS Lambda, or alternative cloud providers. We also share best practices for incorporating observability throughout the development lifecycle.
Unlocking the future of professional services: How Proofpoint uses Amazon Q Business
Proofpoint has redefined its professional services by integrating Amazon Q Business, a fully managed, generative AI powered assistant that you can configure to answer questions, provide summaries, generate content, and complete tasks based on your enterprise data. In this post, we explore how Amazon Q Business transformed Proofpoint’s professional services, detailing its deployment, functionality, and future roadmap.
Deploy Amazon Bedrock Knowledge Bases using Terraform for RAG-based generative AI applications
In this post, we demonstrated how to automate the deployment of Amazon Knowledge Bases for RAG applications using Terraform.
Enhance Geospatial Analysis and GIS Workflows with Amazon Bedrock Capabilities
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.
Introducing Amazon Bedrock AgentCore Gateway: Transforming enterprise AI agent tool development
In this post, we discuss Amazon Bedrock AgentCore Gateway, a fully managed service that revolutionizes how enterprises connect AI agents with tools and services by providing a centralized tool server with unified interface for agent-tool communication. The service offers key capabilities including Security Guard, Translation, Composition, Target extensibility, Infrastructure Manager, and Semantic Tool Selection, while implementing sophisticated dual-sided security architecture for both inbound and outbound connections.
Building a RAG chat-based assistant on Amazon EKS Auto Mode and NVIDIA NIMs
In this post, we demonstrate the implementation of a practical RAG chat-based assistant using a comprehensive stack of modern technologies. The solution uses NVIDIA NIMs for both LLM inference and text embedding services, with the NIM Operator handling their deployment and management. The architecture incorporates Amazon OpenSearch Serverless to store and query high-dimensional vector embeddings for similarity search.
Citations with Amazon Nova understanding models
In this post, we demonstrate how to prompt Amazon Nova understanding models to cite sources in responses. Further, we will also walk through how we can evaluate the responses (and citations) for accuracy.
How Indegene’s AI-powered social intelligence for life sciences turns social media conversations into insights
This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.