Category: Technical How-to

In this post, we show how to build a generative AI solution using Amazon Bedrock that creates bespoke holiday packages by combining customer profiles and preferences with real-time pricing data. We demonstrate how to use Amazon Bedrock Knowledge Bases for travel information, Amazon Bedrock Agents for real-time flight details, and Amazon OpenSearch Serverless for efficient package search and retrieval.

This post explores how to implement a robust monitoring solution for multi-tenant AI deployments using a feature of Amazon Bedrock called application inference profiles. We demonstrate how to create a system that enables granular usage tracking, accurate cost allocation, and dynamic resource management across complex multi-tenant environments.

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.

We’re excited to announce a new integration between Datadog LLM Observability and Amazon Bedrock Agents that helps monitor agentic applications built on Amazon Bedrock. In this post, we’ll explore how Datadog’s LLM Observability provides the visibility and control needed to successfully monitor, operate, and debug production-grade agentic applications built on Amazon Bedrock Agents.

This post is co-written with Andrew Liu, Chelsea Isaac, Zoey Zhang, and Charlie Huang from NVIDIA. DGX Cloud on Amazon Web Services (AWS) represents a significant leap forward in democratizing access to high-performance AI infrastructure. By combining NVIDIA GPU expertise with AWS scalable cloud services, organizations can accelerate their time-to-train, reduce operational complexity, and unlock […]

In this post, we explore how to build a secure RAG application using serverless data lake architecture, an important data strategy to support generative AI development. We use Amazon Web Services (AWS) services including Amazon S3, Amazon DynamoDB, AWS Lambda, and Amazon Bedrock Knowledge Bases to create a comprehensive solution supporting unstructured data assets which can be extended to structured data. The post covers how to implement fine-grained access controls for your enterprise data and design metadata-driven retrieval systems that respect security boundaries. These approaches will help you maximize the value of your organization’s data while maintaining robust security and compliance.

This post presents an end-to-end IDP application powered by Amazon Bedrock Data Automation and other AWS services. It provides a reusable AWS infrastructure as code (IaC) that deploys an IDP pipeline and provides an intuitive UI for transforming documents into structured tables at scale. The application only requires the user to provide the input documents (such as contracts or emails) and a list of attributes to be extracted. It then performs IDP with generative AI.

In this post, we dive into how we integrated Amazon Q in QuickSight to transform natural language requests like “Show me how many items were returned in the US over the past 6 months” into meaningful data visualizations. We demonstrate how combining Amazon Bedrock Agents with Amazon Q in QuickSight creates a comprehensive data assistant that delivers both SQL code and visual insights through a single, intuitive conversational interface—democratizing data access across the enterprise.

In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing content, whether it be through videos, designs, or otherwise, can lead to content moderation challenges. There remains a risk (through intentional or unintentional actions) of inappropriate, offensive, or toxic content being produced and shared. In this post, we cover the use of the multimodal toxicity detection feature of Amazon Bedrock Guardrails to guard against toxic content. Whether you’re an enterprise giant in the fashion industry or an up-and-coming brand, you can use this solution to screen potentially harmful content before it impacts your brand’s reputation and ethical standards. For the purposes of this post, ethical standards refer to toxic, disrespectful, or harmful content and images that could be created by fashion designers.

In this post, we explore how Amazon SageMaker now offers fully managed support for MLflow 3.0, streamlining AI experimentation and accelerating your generative AI journey from idea to production. This release transforms managed MLflow from experiment tracking to providing end-to-end observability, reducing time-to-market for generative AI development.