Category: Advanced (300)

A suspicious transaction report (STR) or suspicious activity report (SAR) is a type of report that a financial organization must submit to a financial regulator if they have reasonable grounds to suspect any financial transaction that has occurred or was attempted during their activities. In this post, we explore a solution that uses FMs available in Amazon Bedrock to create a draft STR.

In this post, we demonstrate how to create a powerful research assistant for drug discovery using Strands Agents and Amazon Bedrock. This AI assistant can search multiple scientific databases simultaneously using the Model Context Protocol (MCP), synthesize its findings, and generate comprehensive reports on drug targets, disease mechanisms, and therapeutic areas.

The repositories for MT-Bench and Arena-Hard were originally developed using OpenAI’s GPT API, primarily employing GPT-4 as the judge. Our team has expanded its functionality by integrating it with the Amazon Bedrock API to enable using Anthropic’s Claude Sonnet on Amazon as judge. In this post, we use both MT-Bench and Arena-Hard to benchmark Amazon Nova models by comparing them to other leading LLMs available through Amazon Bedrock.

In this post, we introduce a solution that uses OpenSearch Service as a vector data store in multi-tenant RAG, achieving data isolation and routing using JWT and FGAC. This solution uses a combination of JWT and FGAC to implement strict tenant data access isolation and routing, necessitating the use of OpenSearch Service.

Amazon Bedrock Knowledge Bases has extended its vector store options by enabling support for Amazon OpenSearch Service managed clusters, further strengthening its capabilities as a fully managed Retrieval Augmented Generation (RAG) solution. This enhancement builds on the core functionality of Amazon Bedrock Knowledge Bases , which is designed to seamlessly connect foundation models (FMs) with internal data sources. This post provides a comprehensive, step-by-step guide on integrating an Amazon Bedrock knowledge base with an OpenSearch Service managed cluster as its vector store.

In this post, we focus on building a Text-to-SQL solution with Amazon Bedrock, a managed service for building generative AI applications. Specifically, we demonstrate the capabilities of Amazon Bedrock Agents. Part 2 explains how we extended the solution to provide business insights using Amazon Q in QuickSight, a business intelligence assistant that answers questions with auto-generated visualizations.

In this post, we discuss permission management strategies, focusing on attribute-based access control (ABAC) patterns that enable granular user access control while minimizing the proliferation of AWS Identity and Access Management (IAM) roles. We also share proven best practices that help organizations maintain security and compliance without sacrificing operational efficiency in their ML workflows.

AI developers and machine learning (ML) engineers can now use the capabilities of Amazon SageMaker Studio directly from their local Visual Studio Code (VS Code). With this capability, you can use your customized local VS Code setup, including AI-assisted development tools, custom extensions, and debugging tools while accessing compute resources and your data in SageMaker Studio. In this post, we show you how to remotely connect your local VS Code to SageMaker Studio development environments to use your customized development environment while accessing Amazon SageMaker AI compute resources.

This post demonstrates building an intelligent AI assistant using Mistral AI models on AWS and MCP, integrating real-time location services, time data, and contextual memory to handle complex multimodal queries. This use case, restaurant recommendations, serves as an example, but this extensible framework can be adapted for enterprise use cases by modifying MCP server configurations to connect with your specific data sources and business systems.

In this post, we demonstrate how to use SageMaker Unified Studio and AWS Identity and Access Management (IAM) to establish a robust permission framework for Amazon Bedrock models. We show how administrators can precisely manage which users and teams have access to specific models within a secure, collaborative environment. We guide you through creating granular permissions to control model access, with code examples for common enterprise governance scenarios.