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Extracting information from unstructured documents at scale is a recurring business task. Common use cases include creating product feature tables from descriptions, extracting metadata from documents, and analyzing legal contracts, customer reviews, news articles, and more. A classic approach to extracting information from text is named entity recognition (NER). NER identifies entities from predefined categories, …

Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation Read More »

In Part 1 of this series, we explored how Amazon’s Worldwide Returns & ReCommerce (WWRR) organization built the Returns & ReCommerce Data Assist (RRDA)—a generative AI solution that transforms natural language questions into validated SQL queries using Amazon Bedrock Agents. Although this capability improves data access for technical users, the WWRR organization’s journey toward truly …

Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight Read More »

What if you could replace hours of data analysis with a minute-long conversation? Large language models can transform how we bridge the gap between business questions and actionable data insights. For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting for the correct data sources to …

Build a conversational data assistant, Part 1: Text-to-SQL with Amazon Bedrock Agents Read More »

Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Although Amazon SageMaker Studio provides user-level execution roles, this approach becomes unwieldy as organizations scale and team sizes grow. Refer to the Operating model whitepaper for …

Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI Read More »

Today, we announce the public preview of long-running execution (asynchronous) flow support within Amazon Bedrock Flows. With Amazon Bedrock Flows, you can link foundation models (FMs), Amazon Bedrock Prompt Management, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, and other AWS services together to build and scale predefined generative AI workflows. As customers …

Long-running execution flows now supported in Amazon Bedrock Flows in public preview Read More »

Fraud detection remains a significant challenge in the financial industry, requiring advanced machine learning (ML) techniques to detect fraudulent patterns while maintaining compliance with strict privacy regulations. Traditional ML models often rely on centralized data aggregation, which raises concerns about data security and regulatory constraints. Fraud cost businesses over $485.6 billion in 2023 alone, according …

Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI Read More »

Voice AI is changing the way we use technology, allowing for more natural and intuitive conversations. Meanwhile, advanced AI agents can now understand complex questions and act autonomously on our behalf. In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and Pipecat, an open source framework for …

Building intelligent AI voice agents with Pipecat and Amazon Bedrock – Part 2 Read More »

The global fashion industry is estimated to be valued at $1.84 trillion in 2025, accounting for approximately 1.63% of the world’s GDP (Statista, 2025). With such massive amounts of generated capital, so too comes the enormous potential for toxic content and misuse. In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing …

Uphold ethical standards in fashion using multimodal toxicity detection with Amazon Bedrock Guardrails Read More »

As AI models become increasingly sophisticated and specialized, the ability to quickly train and customize models can mean the difference between industry leadership and falling behind. That is why hundreds of thousands of customers use the fully managed infrastructure, tools, and workflows of Amazon SageMaker AI to scale and advance AI model development. Since launching …

New capabilities in Amazon SageMaker AI continue to transform how organizations develop AI models Read More »

Amazon SageMaker HyperPod now provides a comprehensive, out-of-the-box dashboard that delivers insights into foundation model (FM) development tasks and cluster resources. This unified observability solution automatically publishes key metrics to Amazon Managed Service for Prometheus and visualizes them in Amazon Managed Grafana dashboards, optimized specifically for FM development with deep coverage of hardware health, resource …

Accelerate foundation model development with one-click observability in Amazon SageMaker HyperPod Read More »