Artificial Intelligence

In Part 1 of our series, we explored the foundational concepts of responsible AI in the payments industry. In this post, we discuss the practical implementation of responsible AI frameworks.

In this post, we show how to process multi-page documents with a human review loop using Amazon Bedrock Data Automation and Amazon SageMaker AI.

In this post, we demonstrate how to build an AI assistant using Amazon Q Business that responds to user requests based on your enterprise documents stored in an S3 bucket, and how the users can use the reference URLs in the AI assistant responses to view or download the referred documents, and verify the AI responses to practice responsible AI.

Today, we are excited to announce the availability of Open AI’s new open weight GPT OSS models, gpt-oss-120b and gpt-oss-20b, from OpenAI in Amazon SageMaker JumpStart. With this launch, you can now deploy OpenAI’s newest reasoning models to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how to get started with these models on SageMaker JumpStart.

Amazon Q Business is a fully managed, generative AI-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q Business, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise systems. […]

In this post, we demonstrate how to track and analyze multi-tenant model inference costs on Amazon Bedrock using the Converse API’s requestMetadata parameter. The solution includes an ETL pipeline using AWS Glue and Amazon QuickSight dashboards to visualize usage patterns, token consumption, and cost allocation across different tenants and departments.

In this post, we highlight how you can deploy multiple generative AI models in Amazon Bedrock to instruct an LLM model to create thematic summaries of text responses. We then show how to use multiple LLM models as a jury to review these LLM-generated summaries and assign a rating to judge the content alignment between the summary title and summary description.

In this post, we explore each component in detail, along with the technical implementation of the two core modules: course outline generation and course content generation.

In this post, we explore how Handmade.com, a leading hand-crafts marketplace, modernized their product description handling by implementing an AI-driven pipeline using Amazon Bedrock and Amazon OpenSearch Service. The solution combines Anthropic’s Claude 3.7 Sonnet LLM for generating descriptions, Amazon Titan Text Embeddings V2 for vector embedding, and semantic search capabilities to automate and enhance product descriptions across their catalog of over 60,000 items.

In this post, we introduce the newly announced Amazon Bedrock AgentCore Browser Tool. We explore why organizations need cloud-based browser automation and the limitations it addresses for FMs that require real-time data access. We talk about key use cases and the core capabilities of the AgentCore Browser Tool. We walk through how to get started with the tool.