AWS Machine Learning Blog
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Train and deploy models on Amazon SageMaker HyperPod using the new HyperPod CLI and SDK
In this post, we demonstrate how to use the new Amazon SageMaker HyperPod CLI and SDK to streamline the process of training and deploying large AI models through practical examples of distributed training using Fully Sharded Data Parallel (FSDP) and model deployment for inference. The tools provide simplified workflows through straightforward commands for common tasks, while offering flexible development options through the SDK for more complex requirements, along with comprehensive observability features and production-ready deployment capabilities.
Build a serverless Amazon Bedrock batch job orchestration workflow using AWS Step Functions
In this post, we introduce a flexible and scalable solution that simplifies the batch inference workflow. This solution provides a highly scalable approach to managing your FM batch inference needs, such as generating embeddings for millions of documents or running custom evaluation or completion tasks with large datasets.
Natural language-based database analytics with Amazon Nova
In this post, we explore how natural language database analytics can revolutionize the way organizations interact with their structured data through the power of large language model (LLM) agents. Natural language interfaces to databases have long been a goal in data management. Agents enhance database analytics by breaking down complex queries into explicit, verifiable reasoning steps and enabling self-correction through validation loops that can catch errors, analyze failures, and refine queries until they accurately match user intent and schema requirements.
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.
Document intelligence evolved: Building and evaluating KIE solutions that scale
In this blog post, we demonstrate an end-to-end approach for building and evaluating a KIE solution using Amazon Nova models available through Amazon Bedrock. This end-to-end approach encompasses three critical phases: data readiness (understanding and preparing your documents), solution development (implementing extraction logic with appropriate models), and performance measurement (evaluating accuracy, efficiency, and cost-effectiveness). We illustrate this comprehensive approach using the FATURA dataset—a collection of diverse invoice documents that serves as a representative proxy for real-world enterprise data.
Announcing the new cluster creation experience for Amazon SageMaker HyperPod
With the new cluster creation experience, you can create your SageMaker HyperPod clusters, including the required prerequisite AWS resources, in one click, with prescriptive default values automatically applied. In this post, we explore the new cluster creation experience for Amazon SageMaker HyperPod.
Detect Amazon Bedrock misconfigurations with Datadog Cloud Security
We’re excited to announce new security capabilities in Datadog Cloud Security that can help you detect and remediate Amazon Bedrock misconfigurations before they become security incidents. This integration helps organizations embed robust security controls and secure their use of the powerful capabilities of Amazon Bedrock by offering three critical advantages: holistic AI security by integrating AI security into your broader cloud security strategy, real-time risk detection through identifying potential AI-related security issues as they emerge, and simplified compliance to help meet evolving AI regulations with pre-built detections.
Set up custom domain names for Amazon Bedrock AgentCore Runtime agents
In this post, we show you how to create custom domain names for your Amazon Bedrock AgentCore Runtime agent endpoints using CloudFront as a reverse proxy. This solution provides several key benefits: simplified integration for development teams, custom domains that align with your organization, cleaner infrastructure abstraction, and straightforward maintenance when endpoints need updates.
Introducing auto scaling on Amazon SageMaker HyperPod
In this post, we announce that Amazon SageMaker HyperPod now supports managed node automatic scaling with Karpenter, enabling efficient scaling of SageMaker HyperPod clusters to meet inference and training demands. We dive into the benefits of Karpenter and provide details on enabling and configuring Karpenter in SageMaker HyperPod EKS clusters.
Meet Boti: The AI assistant transforming how the citizens of Buenos Aires access government information with Amazon Bedrock
This post describes the agentic AI assistant built by the Government of the City of Buenos Aires and the GenAIIC to respond to citizens’ questions about government procedures. The solution consists of two primary components: an input guardrail system that helps prevent the system from responding to harmful user queries and a government procedures agent that retrieves relevant information and generates responses.
Empowering air quality research with secure, ML-driven predictive analytics
In this post, we provide a data imputation solution using Amazon SageMaker AI, AWS Lambda, and AWS Step Functions. This solution is designed for environmental analysts, public health officials, and business intelligence professionals who need reliable PM2.5 data for trend analysis, reporting, and decision-making. We sourced our sample training dataset from openAFRICA. Our solution predicts PM2.5 values using time-series forecasting.
How Amazon Finance built an AI assistant using Amazon Bedrock and Amazon Kendra to support analysts for data discovery and business insights
The Amazon Finance technical team develops and manages comprehensive technology solutions that power financial decision-making and operational efficiency while standardizing across Amazon’s global operations. In this post, we explain how the team conceptualized and implemented a solution to these business challenges by harnessing the power of generative AI using Amazon Bedrock and intelligent search with Amazon Kendra.
Mercury foundation models from Inception Labs are now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart
In this post, we announce that Mercury and Mercury Coder foundation models from Inception Labs are now available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. We demonstrate how to deploy these ultra-fast diffusion-based language models that can generate up to 1,100 tokens per second on NVIDIA H100 GPUs, and showcase their capabilities in code generation and tool use scenarios.
Learn how Amazon Health Services improved discovery in Amazon search using AWS ML and gen AI
In this post, we show you how Amazon Health Services (AHS) solved discoverability challenges on Amazon.com search using AWS services such as Amazon SageMaker, Amazon Bedrock, and Amazon EMR. By combining machine learning (ML), natural language processing, and vector search capabilities, we improved our ability to connect customers with relevant healthcare offerings.
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.
Beyond the basics: A comprehensive foundation model selection framework for generative AI
As the model landscape expands, organizations face complex scenarios when selecting the right foundation model for their applications. In this blog post we present a systematic evaluation methodology for Amazon Bedrock users, combining theoretical frameworks with practical implementation strategies that empower data scientists and machine learning (ML) engineers to make optimal model selections.
Accelerate intelligent document processing with generative AI on AWS
In this post, we introduce our open source GenAI IDP Accelerator—a tested solution that we use to help customers across industries address their document processing challenges. Automated document processing workflows accurately extract structured information from documents, reducing manual effort. We will show you how this ready-to-deploy solution can help you build those workflows with generative AI on AWS in days instead of months.
Amazon SageMaker HyperPod enhances ML infrastructure with scalability and customizability
In this post, we introduced three features in SageMaker HyperPod that enhance scalability and customizability for ML infrastructure. Continuous provisioning offers flexible resource provisioning to help you start training and deploying your models faster and manage your cluster more efficiently. With custom AMIs, you can align your ML environments with organizational security standards and software requirements.
Fine-tune OpenAI GPT-OSS models using Amazon SageMaker HyperPod recipes
This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so you can spin up high-performance clusters on demand. In this post, […]
Inline code nodes now supported in Amazon Bedrock Flows in public preview
We are excited to announce the public preview of support for inline code nodes in Amazon Bedrock Flows. With this powerful new capability, you can write Python scripts directly within your workflow, alleviating the need for separate AWS Lambda functions for simple logic. This feature streamlines preprocessing and postprocessing tasks (like data normalization and response formatting), simplifying generative AI application development and making it more accessible across organizations.
Accelerate enterprise AI implementations with Amazon Q Business
Amazon Q Business offers AWS customers a scalable and comprehensive solution for enhancing business processes across their organization. By carefully evaluating your use cases, following implementation best practices, and using the architectural guidance provided in this post, you can deploy Amazon Q Business to transform your enterprise productivity. The key to success lies in starting small, proving value quickly, and scaling systematically across your organization.
Speed up delivery of ML workloads using Code Editor in Amazon SageMaker Unified Studio
In this post, we walk through how you can use the new Code Editor and multiple spaces support in SageMaker Unified Studio. The sample solution shows how to develop an ML pipeline that automates the typical end-to-end ML activities to build, train, evaluate, and (optionally) deploy an ML model.
How Infosys Topaz leverages Amazon Bedrock to transform technical help desk operations
In this blog, we examine the use case of a large energy supplier whose technical help desk agents answer customer calls and support field agents. We use Amazon Bedrock along with capabilities from Infosys Topaz™ to build a generative AI application that can reduce call handling times, automate tasks, and improve the overall quality of technical support.
Create personalized products and marketing campaigns using Amazon Nova in Amazon Bedrock
Built using Amazon Nova in Amazon Bedrock, The Fragrance Lab represents a comprehensive end-to-end application that illustrates the transformative power of generative AI in retail, consumer goods, advertising, and marketing. In this post, we explore the development of The Fragrance Lab. Our vision was to craft a unique blend of physical and digital experiences that would celebrate creativity, advertising, and consumer goods while capturing the spirit of the French Riviera.
Tyson Foods elevates customer search experience with an AI-powered conversational assistant
In this post, we explore how Tyson Foods collaborated with the AWS Generative AI Innovation Center to revolutionize their customer interaction through an intuitive AI assistant integrated into their website. The AI assistant was built using Amazon Bedrock,
Enhance AI agents using predictive ML models with Amazon SageMaker AI and Model Context Protocol (MCP)
In this post, we demonstrate how to enhance AI agents’ capabilities by integrating predictive ML models using Amazon SageMaker AI and the MCP. By using the open source Strands Agents SDK and the flexible deployment options of SageMaker AI, developers can create sophisticated AI applications that combine conversational AI with powerful predictive analytics capabilities.
Simplify access control and auditing for Amazon SageMaker Studio using trusted identity propagation
In this post, we explore how to enable and use trusted identity propagation in Amazon SageMaker Studio, which allows organizations to simplify access management by granting permissions to existing AWS IAM Identity Center identities. The solution demonstrates how to implement fine-grained access controls based on a physical user's identity, maintain detailed audit logs across supported AWS services, and support long-running user background sessions for training jobs.
Benchmarking document information localization with Amazon Nova
This post demonstrates how to use foundation models (FMs) in Amazon Bedrock, specifically Amazon Nova Pro, to achieve high-accuracy document field localization while dramatically simplifying implementation. We show how these models can precisely locate and interpret document fields with minimal frontend effort, reducing processing errors and manual intervention.
How Infosys built a generative AI solution to process oil and gas drilling data with Amazon Bedrock
We built an advanced RAG solution using Amazon Bedrock leveraging Infosys Topaz™ AI capabilities, tailored for the oil and gas sector. This solution excels in handling multimodal data sources, seamlessly processing text, diagrams, and numerical data while maintaining context and relationships between different data elements. In this post, we provide insights on the solution and walk you through different approaches and architecture patterns explored, like different chunking, multi-vector retrieval, and hybrid search during the development.
Streamline employee training with an intelligent chatbot powered by Amazon Q Business
In this post, we explore how to design and implement custom plugins for Amazon Q Business to create an intelligent chatbot that streamlines employee training by retrieving answers from training materials. The solution implements secure API access using Amazon Cognito for user authentication and authorization, processes multiple document formats, and includes features like RAG-enhanced responses and email escalation capabilities through custom plugins.
Create a travel planning agentic workflow with Amazon Nova
In this post, we explore how to build a travel planning solution using AI agents. The agent uses Amazon Nova, which offers an optimal balance of performance and cost compared to other commercial LLMs. By combining accurate but cost-efficient Amazon Nova models with LangGraph orchestration capabilities, we create a practical travel assistant that can handle complex planning tasks while keeping operational costs manageable for production deployments.
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.
Build a scalable containerized web application on AWS using the MERN stack with Amazon Q Developer – Part 1
In a traditional SDLC, a lot of time is spent in the different phases researching approaches that can deliver on requirements: iterating over design changes, writing, testing and reviewing code, and configuring infrastructure. In this post, you learned about the experience and saw productivity gains you can realize by using Amazon Q Developer as a coding assistant to build a scalable MERN stack web application on AWS.
Optimizing Salesforce’s model endpoints with Amazon SageMaker AI inference components
In this post, we share how the Salesforce AI Platform team optimized GPU utilization, improved resource efficiency and achieved cost savings using Amazon SageMaker AI, specifically inference components.
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.
Introducing Amazon Bedrock AgentCore Identity: Securing agentic AI at scale
In this post, we explore Amazon Bedrock AgentCore Identity, a comprehensive identity and access management service purpose-built for AI agents that enables secure access to AWS resources and third-party tools. The service provides robust identity management features including agent identity directory, agent authorizer, resource credential provider, and resource token vault to help organizations deploy AI agents securely at scale.
Scalable intelligent document processing using Amazon Bedrock Data Automation
In the blog post Scalable intelligent document processing using Amazon Bedrock, we demonstrated how to build a scalable IDP pipeline using Anthropic foundation models on Amazon Bedrock. Although that approach delivered robust performance, the introduction of Amazon Bedrock Data Automation brings a new level of efficiency and flexibility to IDP solutions. This post explores how Amazon Bedrock Data Automation enhances document processing capabilities and streamlines the automation journey.
Whiteboard to cloud in minutes using Amazon Q, Amazon Bedrock Data Automation, and Model Context Protocol
We’re excited to share the Amazon Bedrock Data Automation Model Context Protocol (MCP) server, for seamless integration between Amazon Q and your enterprise data. In this post, you will learn how to use the Amazon Bedrock Data Automation MCP server to securely integrate with AWS Services, use Bedrock Data Automation operations as callable MCP tools, and build a conversational development experience with Amazon Q.
Bringing agentic Retrieval Augmented Generation to Amazon Q Business
In this blog post, we explore how Amazon Q Business is transforming enterprise data interaction through Agentic Retrieval Augmented Generation (RAG).
Empowering students with disabilities: University Startups’ generative AI solution for personalized student pathways
University Startups, headquartered in Bethesda, MD, was founded in 2020 to empower high school students to expand their education beyond a traditional curriculum. University Startups is focused on special education and related services in school districts throughout the US. In this post, we explain how University Startups uses generative AI technology on AWS to enable students to design a specific plan for their future either in education or the work force.
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.
Securely launch and scale your agents and tools on Amazon Bedrock AgentCore Runtime
In this post, we explore how Amazon Bedrock AgentCore Runtime simplifies the deployment and management of AI agents.
PwC and AWS Build Responsible AI with Automated Reasoning on Amazon Bedrock
This post presents how AWS and PwC are developing new reasoning checks that combine deep industry expertise with Automated Reasoning checks in Amazon Bedrock Guardrails to support innovation.
How Amazon scaled Rufus by building multi-node inference using AWS Trainium chips and vLLM
In this post, Amazon shares how they developed a multi-node inference solution for Rufus, their generative AI shopping assistant, using Amazon Trainium chips and vLLM to serve large language models at scale. The solution combines a leader/follower orchestration model, hybrid parallelism strategies, and a multi-node inference unit abstraction layer built on Amazon ECS to deploy models across multiple nodes while maintaining high performance and reliability.
Build an intelligent financial analysis agent with LangGraph and Strands Agents
This post describes an approach of combining three powerful technologies to illustrate an architecture that you can adapt and build upon for your specific financial analysis needs: LangGraph for workflow orchestration, Strands Agents for structured reasoning, and Model Context Protocol (MCP) for tool integration.
Amazon Bedrock AgentCore Memory: Building context-aware agents
In this post, we explore Amazon Bedrock AgentCore Memory, a fully managed service that enables AI agents to maintain both immediate and long-term knowledge, transforming one-off conversations into continuous, evolving relationships between users and AI agents. The service eliminates complex memory infrastructure management while providing full control over what AI agents remember, offering powerful capabilities for maintaining both short-term working memory and long-term intelligent memory across sessions.
Build a conversational natural language interface for Amazon Athena queries using Amazon Nova
In this post, we explore an innovative solution that uses Amazon Bedrock Agents, powered by Amazon Nova Lite, to create a conversational interface for Athena queries. We use AWS Cost and Usage Reports (AWS CUR) as an example, but this solution can be adapted for other databases you query using Athena. This approach democratizes data access while preserving the powerful analytical capabilities of Athena, so you can interact with your data using natural language.
Train and deploy AI models at trillion-parameter scale with Amazon SageMaker HyperPod support for P6e-GB200 UltraServers
In this post, we review the technical specifications of P6e-GB200 UltraServers, discuss their performance benefits, and highlight key use cases. We then walk though how to purchase UltraServer capacity through flexible training plans and get started using UltraServers with SageMaker HyperPod.
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.
Unlocking enhanced legal document review with Lexbe and Amazon Bedrock
In this post, Lexbe, a legal document review software company, demonstrates how they integrated Amazon Bedrock and other AWS services to transform their document review process, enabling legal professionals to instantly query and extract insights from vast volumes of case documents using generative AI. Through collaboration with AWS, Lexbe achieved significant improvements in recall rates, reaching up to 90% by December 2024, and developed capabilities for broad human-style reporting and deep automated inference across multiple languages.
Automate AIOps with SageMaker Unified Studio Projects, Part 2: Technical implementation
In this post, we focus on implementing this architecture with step-by-step guidance and reference code. We provide a detailed technical walkthrough that addresses the needs of two critical personas in the AI development lifecycle: the administrator who establishes governance and infrastructure through automated templates, and the data scientist who uses SageMaker Unified Studio for model development without managing the underlying infrastructure.
Automate AIOps with Amazon SageMaker Unified Studio projects, Part 1: Solution architecture
This post presents architectural strategies and a scalable framework that helps organizations manage multi-tenant environments, automate consistently, and embed governance controls as they scale their AI initiatives with SageMaker Unified Studio.
Demystifying Amazon Bedrock Pricing for a Chatbot Assistant
In this post, we'll look at Amazon Bedrock pricing through the lens of a practical, real-world example: building a customer service chatbot. We'll break down the essential cost components, walk through capacity planning for a mid-sized call center implementation, and provide detailed pricing calculations across different foundation models.
Fine-tune OpenAI GPT-OSS models on Amazon SageMaker AI using Hugging Face libraries
Released on August 5, 2025, OpenAI’s GPT-OSS models, gpt-oss-20b and gpt-oss-120b, are now available on AWS through Amazon SageMaker AI and Amazon Bedrock. In this post, we walk through the process of fine-tuning a GPT-OSS model in a fully managed training environment using SageMaker AI training jobs.
The DIVA logistics agent, powered by Amazon Bedrock
In this post, we discuss how DTDC and ShellKode used Amazon Bedrock to build DIVA 2.0, a generative AI-powered logistics agent.
Automate enterprise workflows by integrating Salesforce Agentforce with Amazon Bedrock Agents
This post explores a practical collaboration, integrating Salesforce Agentforce with Amazon Bedrock Agents and Amazon Redshift, to automate enterprise workflows.
How Amazon Bedrock powers next-generation account planning at AWS
In this post, we share how we built Account Plan Pulse, a generative AI tool designed to streamline and enhance the account planning process, using Amazon Bedrock. Pulse reduces review time and provides actionable account plan summaries for ease of collaboration and consumption, helping AWS sales teams better serve our customers.
Pioneering AI workflows at scale: A deep dive into Asana AI Studio and Amazon Q index collaboration
Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative AI directly into your daily workflows. In this post, we explore how Asana AI Studio and Amazon Q index transform enterprise efficiency through intelligent workflow automation and enhanced data accessibility.
Responsible AI for the payments industry – Part 1
This post explores the unique challenges facing the payments industry in scaling AI adoption, the regulatory considerations that shape implementation decisions, and practical approaches to applying responsible AI principles. In Part 2, we provide practical implementation strategies to operationalize responsible AI within your payment systems.
Responsible AI for the payments industry – Part 2
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.
Process multi-page documents with human review using Amazon Bedrock Data Automation and Amazon SageMaker AI
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.
Build an AI assistant using Amazon Q Business with Amazon S3 clickable URLs
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.
GPT OSS models from OpenAI are now available on SageMaker JumpStart
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.
Discover insights from Microsoft Exchange with the Microsoft Exchange connector for Amazon Q Business
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. […]
AI judging AI: Scaling unstructured text analysis with Amazon Nova
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.
Building an AI-driven course content generation system using Amazon Bedrock
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.
How Handmade.com modernizes product image and description handling with Amazon Bedrock and Amazon OpenSearch Service
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.
Cost tracking multi-tenant model inference on Amazon Bedrock
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.
Introducing Amazon Bedrock AgentCore Browser Tool
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.
Introducing the Amazon Bedrock AgentCore Code Interpreter
In this post, we introduce the Amazon Bedrock AgentCore Code Interpreter, a fully managed service that enables AI agents to securely execute code in isolated sandbox environments. We discuss how the AgentCore Code Interpreter helps solve challenges around security, scalability, and infrastructure management when deploying AI agents that need computational capabilities.
Observing and evaluating AI agentic workflows with Strands Agents SDK and Arize AX
In this post, we present how the Arize AX service can trace and evaluate AI agent tasks initiated through Strands Agents, helping validate the correctness and trustworthiness of agentic workflows.
Building AIOps with Amazon Q Developer CLI and MCP Server
In this post, we discuss how to implement a low-code no-code AIOps solution that helps organizations monitor, identify, and troubleshoot operational events while maintaining their security posture. We show how these technologies work together to automate repetitive tasks, streamline incident response, and enhance operational efficiency across your organization.
Containerize legacy Spring Boot application using Amazon Q Developer CLI and MCP server
In this post, you’ll learn how you can use Amazon Q Developer command line interface (CLI) with Model Context Protocol (MCP) servers integration to modernize a legacy Java Spring Boot application running on premises and then migrate it to Amazon Web Services (AWS) by deploying it on Amazon Elastic Kubernetes Service (Amazon EKS).
Introducing AWS Batch Support for Amazon SageMaker Training jobs
AWS Batch now seamlessly integrates with Amazon SageMaker Training jobs. In this post, we discuss the benefits of managing and prioritizing ML training jobs to use hardware efficiently for your business. We also walk you through how to get started using this new capability and share suggested best practices, including the use of SageMaker training plans.
Structured outputs with Amazon Nova: A guide for builders
We launched constrained decoding to provide reliability when using tools for structured outputs. Now, tools can be used with Amazon Nova foundation models (FMs) to extract data based on complex schemas, reducing tool use errors by over 95%. In this post, we explore how you can use Amazon Nova FMs for structured output use cases.
AI agents unifying structured and unstructured data: Transforming support analytics and beyond with Amazon Q Plugins
Learn how to enhance Amazon Q with custom plugins to combine semantic search capabilities with precise analytics for AWS Support data. This solution enables more accurate answers to analytical questions by integrating structured data querying with RAG architecture, allowing teams to transform raw support cases and health events into actionable insights. Discover how this enhanced architecture delivers exact numerical analysis while maintaining natural language interactions for improved operational decision-making.
Amazon Strands Agents SDK: A technical deep dive into agent architectures and observability
In this post, we first introduce the Strands Agents SDK and its core features. Then we explore how it integrates with AWS environments for secure, scalable deployments, and how it provides rich observability for production use. Finally, we discuss practical use cases, and present a step-by-step example to illustrate Strands in action.
Build dynamic web research agents with the Strands Agents SDK and Tavily
In this post, we introduce how to combine Strands Agents with Tavily’s purpose-built web intelligence API, to create powerful research agents that excel at complex information gathering tasks while maintaining the security and compliance standards required for enterprise deployment.
Automate the creation of handout notes using Amazon Bedrock Data Automation
In this post, we show how you can build an automated, serverless solution to transform webinar recordings into comprehensive handouts using Amazon Bedrock Data Automation for video analysis. We walk you through the implementation of Amazon Bedrock Data Automation to transcribe and detect slide changes, as well as the use of Amazon Bedrock foundation models (FMs) for transcription refinement, combined with custom AWS Lambda functions orchestrated by AWS Step Functions.
Streamline GitHub workflows with generative AI using Amazon Bedrock and MCP
This blog post explores how to create powerful agentic applications using the Amazon Bedrock FMs, LangGraph, and the Model Context Protocol (MCP), with a practical scenario of handling a GitHub workflow of issue analysis, code fixes, and pull request generation.
Mistral-Small-3.2-24B-Instruct-2506 is now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart
Today, we’re excited to announce that Mistral-Small-3.2-24B-Instruct-2506—a 24-billion-parameter large language model (LLM) from Mistral AI that’s optimized for enhanced instruction following and reduced repetition errors—is available for customers through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace. Amazon Bedrock Marketplace is a capability in Amazon Bedrock that developers can use to discover, test, and use over […]
Generate suspicious transaction report drafts for financial compliance using generative AI
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.
Fine-tune and deploy Meta Llama 3.2 Vision for generative AI-powered web automation using AWS DLCs, Amazon EKS, and Amazon Bedrock
In this post, we present a complete solution for fine-tuning and deploying the Llama-3.2-11B-Vision-Instruct model for web automation tasks. We demonstrate how to build a secure, scalable, and efficient infrastructure using AWS Deep Learning Containers (DLCs) on Amazon Elastic Kubernetes Service (Amazon EKS).
How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock
In this post, we examine a solution adopted by Nippon Life India Asset Management Limited that improves the accuracy of the response over a regular (naive) RAG approach by rewriting the user queries and aggregating and reranking the responses. The proposed solution uses enhanced RAG methods such as reranking to improve the overall accuracy
Build a drug discovery research assistant using Strands Agents and Amazon Bedrock
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.
Amazon Nova Act SDK (preview): Path to production for browser automation agents
In this post, we’ll walk through what makes Nova Act SDK unique, how it works, and how teams across industries are already using it to automate browser-based workflows at scale.
Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency
In this post, we explore how Crypto.com used user and system feedback to continuously improve and optimize our instruction prompts. This feedback-driven approach has enabled us to create more effective prompts that adapt to various subsystems while maintaining high performance across different use cases.
Build modern serverless solutions following best practices using Amazon Q Developer CLI and MCP
This post explores how the AWS Serverless MCP server accelerates development throughout the serverless lifecycle, from making architectural decisions with tools like get_iac_guidance and get_lambda_guidance, to streamlining development with get_serverless_templates, sam_init, to deployment with SAM integration, webapp_deployment_help, and configure_domain. We show how this conversational AI approach transforms the entire process, from architecture design through operations, dramatically accelerating AWS serverless projects while adhering to architectural principles.
Build an intelligent eDiscovery solution using Amazon Bedrock Agents
In this post, we demonstrate how to build an intelligent eDiscovery solution using Amazon Bedrock Agents for real-time document analysis. We show how to deploy specialized agents for document classification, contract analysis, email review, and legal document processing, all working together through a multi-agent architecture. We walk through the implementation details, deployment steps, and best practices to create an extensible foundation that organizations can adapt to their specific eDiscovery requirements.
How PerformLine uses prompt engineering on Amazon Bedrock to detect compliance violations
PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. In this post, PerformLine and AWS explore how PerformLine used Amazon Bedrock to accelerate compliance processes, generate actionable insights, and provide contextual data—delivering the speed and accuracy essential for large-scale oversight.
Boost cold-start recommendations with vLLM on AWS Trainium
In this post, we demonstrate how to use vLLM for scalable inference and use AWS Deep Learning Containers (DLC) to streamline model packaging and deployment. We’ll generate interest expansions through structured prompts, encode them into embeddings, retrieve candidates with FAISS, apply validation to keep results grounded, and frame the cold-start challenge as a scientific experiment—benchmarking LLM and encoder pairings, iterating rapidly on recommendation metrics, and showing clear ROI for each configuration
Benchmarking Amazon Nova: A comprehensive analysis through MT-Bench and Arena-Hard-Auto
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.
Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization
At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this post, we present a streamlined approach to customize Nova Micro in SageMaker training jobs.
Multi-tenant RAG implementation with Amazon Bedrock and Amazon OpenSearch Service for SaaS using JWT
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.
Enhance generative AI solutions using Amazon Q index with Model Context Protocol – Part 1
In this post, we explore best practices and integration patterns for combining Amazon Q index and MCP, enabling enterprises to build secure, scalable, and actionable AI search-and-retrieval architectures.
Beyond accelerators: Lessons from building foundation models on AWS with Japan’s GENIAC program
In 2024, the Ministry of Economy, Trade and Industry (METI) launched the Generative AI Accelerator Challenge (GENIAC)—a Japanese national program to boost generative AI by providing companies with funding, mentorship, and massive compute resources for foundation model (FM) development. AWS was selected as the cloud provider for GENIAC’s second cycle (cycle 2). It provided infrastructure and technical guidance for 12 participating organizations.
Streamline deep learning environments with Amazon Q Developer and MCP
In this post, we explore how to use Amazon Q Developer and Model Context Protocol (MCP) servers to streamline DLC workflows to automate creation, execution, and customization of DLC containers.
Build an AI-powered automated summarization system with Amazon Bedrock and Amazon Transcribe using Terraform
This post introduces a serverless meeting summarization system that harnesses the advanced capabilities of Amazon Bedrock and Amazon Transcribe to transform audio recordings into concise, structured, and actionable summaries. By automating this process, organizations can reclaim countless hours while making sure key insights, action items, and decisions are systematically captured and made accessible to stakeholders.
Kyruus builds a generative AI provider matching solution on AWS
In this post, we demonstrate how Kyruus Health uses AWS services to build Guide. We show how Amazon Bedrock, a fully managed service that provides access to foundation models (FMs) from leading AI companies and Amazon through a single API, and Amazon OpenSearch Service, a managed search and analytics service, work together to understand everyday language about health concerns and connect members with the right providers.
Use generative AI in Amazon Bedrock for enhanced recommendation generation in equipment maintenance
In the manufacturing world, valuable insights from service reports often remain underutilized in document storage systems. This post explores how Amazon Web Services (AWS) customers can build a solution that automates the digitisation and extraction of crucial information from many reports using generative AI.
Build real-time travel recommendations using AI agents on Amazon Bedrock
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.
Deploy a full stack voice AI agent with Amazon Nova Sonic
In this post, we show how to create an AI-powered call center agent for a fictional company called AnyTelco. The agent, named Telly, can handle customer inquiries about plans and services while accessing real-time customer data using custom tools implemented with the Model Context Protocol (MCP) framework.
Manage multi-tenant Amazon Bedrock costs using application inference profiles
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.
Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI
Evaluating the performance of large language models (LLMs) goes beyond statistical metrics like perplexity or bilingual evaluation understudy (BLEU) scores. For most real-world generative AI scenarios, it’s crucial to understand whether a model is producing better outputs than a baseline or an earlier iteration. This is especially important for applications such as summarization, content generation, […]
Building cost-effective RAG applications with Amazon Bedrock Knowledge Bases and Amazon S3 Vectors
In this post, we demonstrate how to integrate Amazon S3 Vectors with Amazon Bedrock Knowledge Bases for RAG applications. You'll learn a practical approach to scale your knowledge bases to handle millions of documents while maintaining retrieval quality and using S3 Vectors cost-effective storage.
Implementing on-demand deployment with customized Amazon Nova models on Amazon Bedrock
In this post, we walk through the custom model on-demand deployment workflow for Amazon Bedrock and provide step-by-step implementation guides using both the AWS Management Console and APIs or AWS SDKs. We also discuss best practices and considerations for deploying customized Amazon Nova models on Amazon Bedrock.
Building enterprise-scale RAG applications with Amazon S3 Vectors and DeepSeek R1 on Amazon SageMaker AI
Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI, […]
Accenture scales video analysis with Amazon Nova and Amazon Bedrock Agents
This post was written with Ilan Geller, Kamal Mannar, Debasmita Ghosh, and Nakul Aggarwal of Accenture. Video highlights offer a powerful way to boost audience engagement and extend content value for content publishers. These short, high-impact clips capture key moments that drive viewer retention, amplify reach across social media, reinforce brand identity, and open new […]
Deploy conversational agents with Vonage and Amazon Nova Sonic
In this post, we explore how developers can integrate Amazon Nova Sonic with the Vonage communications service to build responsive, natural-sounding voice experiences in real time. By combining the Vonage Voice API with the low-latency and expressive speech capabilities of Amazon Nova Sonic, businesses can deploy AI voice agents that deliver more human-like interactions than traditional voice interfaces. These agents can be used as customer support, virtual assistants, and more.
Enabling customers to deliver production-ready AI agents at scale
Today, I’m excited to share how we’re bringing this vision to life with new capabilities that address the fundamental aspects of building and deploying agents at scale. These innovations will help you move beyond experiments to production-ready agent systems that can be trusted with your most critical business processes.
Amazon Bedrock Knowledge Bases now supports Amazon OpenSearch Service Managed Cluster as vector store
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.
Monitor agents built on Amazon Bedrock with Datadog LLM Observability
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.
How PayU built a secure enterprise AI assistant using Amazon Bedrock
PayU offers a full-stack digital financial services system that serves the financial needs of merchants, banks, and consumers through technology. In this post, we explain how we equipped the PayU team with an enterprise AI solution and democratized AI access using Amazon Bedrock, without compromising on data residency requirements.
Supercharge generative AI workflows with NVIDIA DGX Cloud on AWS and Amazon Bedrock Custom Model Import
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 […]
Accelerate generative AI inference with NVIDIA Dynamo and Amazon EKS
This post introduces NVIDIA Dynamo and explains how to set it up on Amazon EKS for automated scaling and streamlined Kubernetes operations. We provide a hands-on walkthrough, which uses the NVIDIA Dynamo blueprint on the AI on EKS GitHub repo by AWS Labs to provision the infrastructure, configure monitoring, and install the NVIDIA Dynamo operator.
AWS doubles investment in AWS Generative AI Innovation Center, marking two years of customer success
In this post, AWS announces a $100 million additional investment in its AWS Generative AI Innovation Center, marking two years of successful customer collaborations across industries from financial services to healthcare. The investment comes as AI evolves toward more autonomous, agentic systems, with the center already helping thousands of customers drive millions in productivity gains and transform customer experiences.
Build AI-driven policy creation for vehicle data collection and automation using Amazon Bedrock
Sonatus partnered with the AWS Generative AI Innovation Center to develop a natural language interface to generate data collection and automation policies using generative AI. This innovation aims to reduce the policy generation process from days to minutes while making it accessible to both engineers and non-experts alike. In this post, we explore how we built this system using Sonatus’s Collector AI and Amazon Bedrock. We discuss the background, challenges, and high-level solution architecture.
How Rapid7 automates vulnerability risk scores with ML pipelines using Amazon SageMaker AI
In this post, we share how Rapid7 implemented end-to-end automation for the training, validation, and deployment of ML models that predict CVSS vectors. Rapid7 customers have the information they need to accurately understand their risk and prioritize remediation measures.
Build secure RAG applications with AWS serverless data lakes
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.
Advanced fine-tuning methods on Amazon SageMaker AI
When fine-tuning ML models on AWS, you can choose the right tool for your specific needs. AWS provides a comprehensive suite of tools for data scientists, ML engineers, and business users to achieve their ML goals. AWS has built solutions to support various levels of ML sophistication, from simple SageMaker training jobs for FM fine-tuning to the power of SageMaker HyperPod for cutting-edge research. We invite you to explore these options, starting with what suits your current needs, and evolve your approach as those needs change.
Streamline machine learning workflows with SkyPilot on Amazon SageMaker HyperPod
This post is co-written with Zhanghao Wu, co-creator of SkyPilot. The rapid advancement of generative AI and foundation models (FMs) has significantly increased computational resource requirements for machine learning (ML) workloads. Modern ML pipelines require efficient systems for distributing workloads across accelerated compute resources, while making sure developer productivity remains high. Organizations need infrastructure solutions […]
Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation
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.
Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight
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.
Build a conversational data assistant, Part 1: Text-to-SQL with Amazon Bedrock Agents
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.
Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI
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.
Long-running execution flows now supported in Amazon Bedrock Flows in public preview
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.
Fraud detection empowered by federated learning with the Flower framework on Amazon SageMaker AI
In this post, we explore how SageMaker and federated learning help financial institutions build scalable, privacy-first fraud detection systems.
Building intelligent AI voice agents with Pipecat and Amazon Bedrock – Part 2
In Part 1 of this series, you learned how you can use the combination of Amazon Bedrock and Pipecat, an open source framework for voice and multimodal conversational AI agents to build applications with human-like conversational AI. You learned about common use cases of voice agents and the cascaded models approach, where you orchestrate several components to build your voice AI agent. In this post (Part 2), you explore how to use speech-to-speech foundation model, Amazon Nova Sonic, and the benefits of using a unified model.
Uphold ethical standards in fashion using multimodal toxicity detection with Amazon Bedrock Guardrails
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.
New capabilities in Amazon SageMaker AI continue to transform how organizations develop AI models
In this post, we share some of the new innovations in SageMaker AI that can accelerate how you build and train AI models. These innovations include new observability capabilities in SageMaker HyperPod, the ability to deploy JumpStart models on HyperPod, remote connections to SageMaker AI from local development environments, and fully managed MLflow 3.0.
Accelerate foundation model development with one-click observability in Amazon SageMaker HyperPod
With a one-click installation of the Amazon Elastic Kubernetes Service (Amazon EKS) add-on for SageMaker HyperPod observability, you can consolidate health and performance data from NVIDIA DCGM, instance-level Kubernetes node exporters, Elastic Fabric Adapter (EFA), integrated file systems, Kubernetes APIs, Kueue, and SageMaker HyperPod task operators. In this post, we walk you through installing and using the unified dashboards of the out-of-the-box observability feature in SageMaker HyperPod. We cover the one-click installation from the Amazon SageMaker AI console, navigating the dashboard and metrics it consolidates, and advanced topics such as setting up custom alerts.
Accelerating generative AI development with fully managed MLflow 3.0 on Amazon SageMaker AI
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.
Amazon SageMaker HyperPod launches model deployments to accelerate the generative AI model development lifecycle
In this post, we announce Amazon SageMaker HyperPod support for deploying foundation models from SageMaker JumpStart, as well as custom or fine-tuned models from Amazon S3 or Amazon FSx. This new capability allows customers to train, fine-tune, and deploy models on the same HyperPod compute resources, maximizing resource utilization across the entire model lifecycle.
Supercharge your AI workflows by connecting to SageMaker Studio from Visual Studio Code
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.
Use K8sGPT and Amazon Bedrock for simplified Kubernetes cluster maintenance
This post demonstrates the best practices to run K8sGPT in AWS with Amazon Bedrock in two modes: K8sGPT CLI and K8sGPT Operator. It showcases how the solution can help SREs simplify Kubernetes cluster management through continuous monitoring and operational intelligence.
How Rocket streamlines the home buying experience with Amazon Bedrock Agents
Rocket AI Agent is more than a digital assistant. It’s a reimagined approach to client engagement, powered by agentic AI. By combining Amazon Bedrock Agents with Rocket’s proprietary data and backend systems, Rocket has created a smarter, more scalable, and more human experience available 24/7, without the wait. This post explores how Rocket brought that vision to life using Amazon Bedrock Agents, powering a new era of AI-driven support that is consistently available, deeply personalized, and built to take action.
Build an MCP application with Mistral models on AWS
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.
Build real-time conversational AI experiences using Amazon Nova Sonic and LiveKit
mazon Nova Sonic is now integrated with LiveKit’s WebRTC framework, a widely used platform that enables developers to build real-time audio, video, and data communication applications. This integration makes it possible for developers to build conversational voice interfaces without needing to manage complex audio pipelines or signaling protocols. In this post, we explain how this integration works, how it addresses the historical challenges of voice-first applications, and some initial steps to start using this solution.
AWS AI infrastructure with NVIDIA Blackwell: Two powerful compute solutions for the next frontier of AI
In this post, we announce general availability of Amazon EC2 P6e-GB200 UltraServers and P6-B200 instances, powered by NVIDIA Blackwell GPUs, designed for training and deploying the largest, most sophisticated AI models.
Unlock retail intelligence by transforming data into actionable insights using generative AI with Amazon Q Business
Amazon Q Business for Retail Intelligence is an AI-powered assistant designed to help retail businesses streamline operations, improve customer service, and enhance decision-making processes. This solution is specifically engineered to be scalable and adaptable to businesses of various sizes, helping them compete more effectively. In this post, we show how you can use Amazon Q Business for Retail Intelligence to transform your data into actionable insights.
Democratize data for timely decisions with text-to-SQL at Parcel Perform
The business team in Parcel Perform often needs access to data to answer questions related to merchants’ parcel deliveries, such as “Did we see a spike in delivery delays last week? If so, in which transit facilities were this observed, and what was the primary cause of the issue?” Previously, the data team had to manually form the query and run it to fetch the data. With the new generative AI-powered text-to-SQL capability in Parcel Perform, the business team can self-serve their data needs by using an AI assistant interface. In this post, we discuss how Parcel Perform incorporated generative AI, data storage, and data access through AWS services to make timely decisions.
Query Amazon Aurora PostgreSQL using Amazon Bedrock Knowledge Bases structured data
In this post, we discuss how to make your Amazon Aurora PostgreSQL-Compatible Edition data available for natural language querying through Amazon Bedrock Knowledge Bases while maintaining data freshness.
Configure fine-grained access to Amazon Bedrock models using Amazon SageMaker Unified Studio
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.
Improve conversational AI response times for enterprise applications with the Amazon Bedrock streaming API and AWS AppSync
This post demonstrates how integrating an Amazon Bedrock streaming API with AWS AppSync subscriptions significantly enhances AI assistant responsiveness and user satisfaction. By implementing this streaming approach, the global financial services organization reduced initial response times for complex queries by approximately 75%—from 10 seconds to just 2–3 seconds—empowering users to view responses as they’re generated rather than waiting for complete answers.
Scale generative AI use cases, Part 1: Multi-tenant hub and spoke architecture using AWS Transit Gateway
n this two-part series, we discuss a hub and spoke architecture pattern for building a multi-tenant and multi-account architecture. This pattern supports abstractions for shared services across use cases and teams, helping create secure, scalable, and reliable generative AI systems. In Part 1, we present a centralized hub for generative AI service abstractions and tenant-specific spokes, using AWS Transit Gateway for cross-account interoperability.
Accelerate AI development with Amazon Bedrock API keys
Today, we’re excited to announce a significant improvement to the developer experience of Amazon Bedrock: API keys. API keys provide quick access to the Amazon Bedrock APIs, streamlining the authentication process so that developers can focus on building rather than configuration.
Accelerating data science innovation: How Bayer Crop Science used AWS AI/ML services to build their next-generation MLOps service
In this post, we show how Bayer Crop Science manages large-scale data science operations by training models for their data analytics needs and maintaining high-quality code documentation to support developers. Through these solutions, Bayer Crop Science projects up to a 70% reduction in developer onboarding time and up to a 30% improvement in developer productivity.
Combat financial fraud with GraphRAG on Amazon Bedrock Knowledge Bases
In this post, we show how to use Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics to build a financial fraud detection solution.
Classify call center conversations with Amazon Bedrock batch inference
In this post, we demonstrate how to build an end-to-end solution for text classification using the Amazon Bedrock batch inference capability with the Anthropic’s Claude Haiku model. We walk through classifying travel agency call center conversations into categories, showcasing how to generate synthetic training data, process large volumes of text data, and automate the entire workflow using AWS services.
Effective cross-lingual LLM evaluation with Amazon Bedrock
In this post, we demonstrate how to use the evaluation features of Amazon Bedrock to deliver reliable results across language barriers without the need for localized prompts or custom infrastructure. Through comprehensive testing and analysis, we share practical strategies to help reduce the cost and complexity of multilingual evaluation while maintaining high standards across global large language model (LLM) deployments.
Cohere Embed 4 multimodal embeddings model is now available on Amazon SageMaker JumpStart
The Cohere Embed 4 multimodal embeddings model is now generally available on Amazon SageMaker JumpStart. The Embed 4 model is built for multimodal business documents, has leading multilingual capabilities, and offers notable improvement over Embed 3 across key benchmarks. In this post, we discuss the benefits and capabilities of this new model. We also walk you through how to deploy and use the Embed 4 model using SageMaker JumpStart.
How INRIX accelerates transportation planning with Amazon Bedrock
INRIX pioneered the use of GPS data from connected vehicles for transportation intelligence. In this post, we partnered with Amazon Web Services (AWS) customer INRIX to demonstrate how Amazon Bedrock can be used to determine the best countermeasures for specific city locations using rich transportation data and how such countermeasures can be automatically visualized in street view images. This approach allows for significant planning acceleration compared to traditional approaches using conceptual drawings.
Qwen3 family of reasoning models now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart
Today, we are excited to announce that Qwen3, the latest generation of large language models (LLMs) in the Qwen family, is available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Qwen3 models—available in 0.6B, 4B, 8B, and 32B parameter sizes—to build, experiment, and responsibly scale your generative AI applications on AWS. In this post, we demonstrate how to get started with Qwen3 on Amazon Bedrock Marketplace and SageMaker JumpStart.
Agents as escalators: Real-time AI video monitoring with Amazon Bedrock Agents and video streams
In this post, we show how to build a fully deployable solution that processes video streams using OpenCV, Amazon Bedrock for contextual scene understanding and automated responses through Amazon Bedrock Agents. This solution extends the capabilities demonstrated in Automate chatbot for document and data retrieval using Amazon Bedrock Agents and Knowledge Bases, which discussed using Amazon Bedrock Agents for document and data retrieval. In this post, we apply Amazon Bedrock Agents to real-time video analysis and event monitoring.
Transforming network operations with AI: How Swisscom built a network assistant using Amazon Bedrock
In this post, we explore how Swisscom developed their Network Assistant. We discuss the initial challenges and how they implemented a solution that delivers measurable benefits. We examine the technical architecture, discuss key learnings, and look at future enhancements that can further transform network operations.
End-to-End model training and deployment with Amazon SageMaker Unified Studio
In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using SageMaker AI inference for real-time inference. We also discuss best practices to choose the right instance size and share some debugging best practices while working with JupyterLab notebooks in SageMaker Unified Studio.
Optimize RAG in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service
In this post, we show how to use Amazon OpenSearch Service as a vector store to build an efficient RAG application.
Advancing AI agent governance with Boomi and AWS: A unified approach to observability and compliance
In this post, we share how Boomi partnered with AWS to help enterprises accelerate and scale AI adoption with confidence using Agent Control Tower.
Use Amazon SageMaker Unified Studio to build complex AI workflows using Amazon Bedrock Flows
In this post, we demonstrate how you can use SageMaker Unified Studio to create complex AI workflows using Amazon Bedrock Flows.
Accelerating AI innovation: Scale MCP servers for enterprise workloads with Amazon Bedrock
In this post, we present a centralized Model Context Protocol (MCP) server implementation using Amazon Bedrock that provides shared access to tools and resources for enterprise AI workloads. The solution enables organizations to accelerate AI innovation by standardizing access to resources and tools through MCP, while maintaining security and governance through a centralized approach.
Choosing the right approach for generative AI-powered structured data retrieval
In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in AWS, including direct conversational interfaces, BI tool enhancements, and custom text-to-SQL solutions.
Revolutionizing drug data analysis using Amazon Bedrock multimodal RAG capabilities
In this post, we explore how Amazon Bedrock's multimodal RAG capabilities revolutionize drug data analysis by efficiently processing complex medical documentation containing text, images, graphs, and tables.
Build and deploy AI inference workflows with new enhancements to the Amazon SageMaker Python SDK
In this post, we provide an overview of the user experience, detailing how to set up and deploy these workflows with multiple models using the SageMaker Python SDK. We walk through examples of building complex inference workflows, deploying them to SageMaker endpoints, and invoking them for real-time inference.
Context extraction from image files in Amazon Q Business using LLMs
In this post, we look at a step-by-step implementation for using the custom document enrichment (CDE) feature within an Amazon Q Business application to process standalone image files. We walk you through an AWS Lambda function configured within CDE to process various image file types, and showcase an example scenario of how this integration enhances Amazon Q Business's ability to provide comprehensive insights.
AWS costs estimation using Amazon Q CLI and AWS Cost Analysis MCP
In this post, we explore how to use Amazon Q CLI with the AWS Cost Analysis MCP server to perform sophisticated cost analysis that follows AWS best practices. We discuss basic setup and advanced techniques, with detailed examples and step-by-step instructions.
Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails
In this post, we introduce the new safeguard tiers available in Amazon Bedrock Guardrails, explain their benefits and use cases, and provide guidance on how to implement and evaluate them in your AI applications.
Structured data response with Amazon Bedrock: Prompt Engineering and Tool Use
We demonstrate two methods for generating structured responses with Amazon Bedrock: Prompt Engineering and Tool Use with the Converse API. Prompt Engineering is flexible, works with Bedrock models (including those without Tool Use support), and handles various schema types (e.g., Open API schemas), making it a great starting point. Tool Use offers greater reliability, consistent results, seamless API integration, and runtime validation of JSON schema for enhanced control.
Using Amazon SageMaker AI Random Cut Forest for NASA’s Blue Origin spacecraft sensor data
In this post, we demonstrate how to use SageMaker AI to apply the Random Cut Forest (RCF) algorithm to detect anomalies in spacecraft position, velocity, and quaternion orientation data from NASA and Blue Origin’s demonstration of lunar Deorbit, Descent, and Landing Sensors (BODDL-TP).
Build an intelligent multi-agent business expert using Amazon Bedrock
In this post, we demonstrate how to build a multi-agent system using multi-agent collaboration in Amazon Bedrock Agents to solve complex business questions in the biopharmaceutical industry. We show how specialized agents in research and development (R&D), legal, and finance domains can work together to provide comprehensive business insights by analyzing data from multiple sources.
Driving cost-efficiency and speed in claims data processing with Amazon Nova Micro and Amazon Nova Lite
In this post, we shared how an internal technology team at Amazon evaluated Amazon Nova models, resulting in notable improvements in inference speed and cost-efficiency.
Power Your LLM Training and Evaluation with the New SageMaker AI Generative AI Tools
Today we are excited to introduce the Text Ranking and Question and Answer UI templates to SageMaker AI customers. In this blog post, we’ll walk you through how to set up these templates in SageMaker to create high-quality datasets for training your large language models.
Amazon Bedrock Agents observability using Arize AI
Today, we’re excited to announce a new integration between Arize AI and Amazon Bedrock Agents that addresses one of the most significant challenges in AI development: observability. In this post, we demonstrate the Arize Phoenix system for tracing and evaluation.
How SkillShow automates youth sports video processing using Amazon Transcribe
SkillShow, a leader in youth sports video production, films over 300 events yearly in the youth sports industry, creating content for over 20,000 young athletes annually. This post describes how SkillShow used Amazon Transcribe and other Amazon Web Services (AWS) machine learning (ML) services to automate their video processing workflow, reducing editing time and costs while scaling their operations.
NewDay builds A Generative AI based Customer service Agent Assist with over 90% accuracy
This post is co-written with Sergio Zavota and Amy Perring from NewDay. NewDay has a clear and defining purpose: to help people move forward with credit. NewDay provides around 4 million customers access to credit responsibly and delivers exceptional customer experiences, powered by their in-house technology system. NewDay’s contact center handles 2.5 million calls annually, […]
No-code data preparation for time series forecasting using Amazon SageMaker Canvas
Amazon SageMaker Canvas offers no-code solutions that simplify data wrangling, making time series forecasting accessible to all users regardless of their technical background. In this post, we explore how SageMaker Canvas and SageMaker Data Wrangler provide no-code data preparation techniques that empower users of all backgrounds to prepare data and build time series forecasting models in a single interface with confidence.
Build an agentic multimodal AI assistant with Amazon Nova and Amazon Bedrock Data Automation
In this post, we demonstrate how agentic workflow patterns such as Retrieval Augmented Generation (RAG), multi-tool orchestration, and conditional routing with LangGraph enable end-to-end solutions that artificial intelligence and machine learning (AI/ML) developers and enterprise architects can adopt and extend. We walk through an example of a financial management AI assistant that can provide quantitative research and grounded financial advice by analyzing both the earnings call (audio) and the presentation slides (images), along with relevant financial data feeds.
Build a scalable AI video generator using Amazon SageMaker AI and CogVideoX
In recent years, the rapid advancement of artificial intelligence and machine learning (AI/ML) technologies has revolutionized various aspects of digital content creation. One particularly exciting development is the emergence of video generation capabilities, which offer unprecedented opportunities for companies across diverse industries. This technology allows for the creation of short video clips that can be […]
Building trust in AI: The AWS approach to the EU AI Act
The EU AI Act establishes comprehensive regulations for AI development and deployment within the EU. AWS is committed to building trust in AI through various initiatives including being among the first signatories of the EU's AI Pact, providing AI Service Cards and guardrails, and offering educational resources while helping customers understand their responsibilities under the new regulatory framework.
Update on the AWS DeepRacer Student Portal
Starting July 14, 2025, the AWS DeepRacer Student Portal will enter a maintenance phase where new registrations will be disabled. Until September 15, 2025, existing users will retain full access to their content and training materials, with updates limited to critical security fixes, after which the portal will no longer be available.
Accelerate foundation model training and inference with Amazon SageMaker HyperPod and Amazon SageMaker Studio
In this post, we discuss how SageMaker HyperPod and SageMaker Studio can improve and speed up the development experience of data scientists by using IDEs and tooling of SageMaker Studio and the scalability and resiliency of SageMaker HyperPod with Amazon EKS. The solution simplifies the setup for the system administrator of the centralized system by using the governance and security capabilities offered by the AWS services.
Meeting summarization and action item extraction with Amazon Nova
In this post, we present a benchmark of different understanding models from the Amazon Nova family available on Amazon Bedrock, to provide insights on how you can choose the best model for a meeting summarization task.
Building a custom text-to-SQL agent using Amazon Bedrock and Converse API
Developing robust text-to-SQL capabilities is a critical challenge in the field of natural language processing (NLP) and database management. The complexity of NLP and database management increases in this field, particularly while dealing with complex queries and database structures. In this post, we introduce a straightforward but powerful solution with accompanying code to text-to-SQL using a custom agent implementation along with Amazon Bedrock and Converse API.
Accelerate threat modeling with generative AI
In this post, we explore how generative AI can revolutionize threat modeling practices by automating vulnerability identification, generating comprehensive attack scenarios, and providing contextual mitigation strategies.
How Anomalo solves unstructured data quality issues to deliver trusted assets for AI with AWS
In this post, we explore how you can use Anomalo with Amazon Web Services (AWS) AI and machine learning (AI/ML) to profile, validate, and cleanse unstructured data collections to transform your data lake into a trusted source for production ready AI initiatives.
An innovative financial services leader finds the right AI solution: Robinhood and Amazon Nova
In this post, we share how Robinhood delivers democratized finance and real-time market insights using generative AI and Amazon Nova.
Build conversational interfaces for structured data using Amazon Bedrock Knowledge Bases
This post provides instructions to configure a structured data retrieval solution, with practical code examples and templates. It covers implementation samples and additional considerations, empowering you to quickly build and scale your conversational data interfaces.