AWS Machine Learning Blog
aws.amazon.com/jp/blogs/machine-learning/
Metagenomi generates millions of novel enzymes cost-effectively using AWS Inferentia
In this post, we detail how Metagenomi partnered with AWS to implement the Progen2 protein language model on AWS Inferentia, achieving up to 56% cost reduction for high-throughput enzyme generation workflows. The implementation enabled cost-effective generation of millions of novel enzyme variants using EC2 Inf2 Spot Instances and AWS Batch, demonstrating how cloud-based generative AI can make large-scale protein design more accessible for biotechnology applications .
Serverless deployment for your Amazon SageMaker Canvas models
In this post, we walk through how to take an ML model built in SageMaker Canvas and deploy it using SageMaker Serverless Inference, helping you go from model creation to production-ready predictions quickly and efficiently without managing any infrastructure. This solution demonstrates a complete workflow from adding your trained model to the SageMaker Model Registry through creating serverless endpoint configurations and deploying endpoints that automatically scale based on demand .
Building a multi-agent voice assistant with Amazon Nova Sonic and Amazon Bedrock AgentCore
In this post, we explore how Amazon Nova Sonic's speech-to-speech capabilities can be combined with Amazon Bedrock AgentCore to create sophisticated multi-agent voice assistants that break complex tasks into specialized, manageable components. The approach demonstrates how to build modular, scalable voice applications using a banking assistant example with dedicated sub-agents for authentication, banking inquiries, and mortgage services, offering a more maintainable alternative to monolithic voice assistant designs.
Accelerate large-scale AI training with Amazon SageMaker HyperPod training operator
In this post, we demonstrate how to deploy and manage machine learning training workloads using the Amazon SageMaker HyperPod training operator, which enhances training resilience for Kubernetes workloads through pinpoint recovery and customizable monitoring capabilities. The Amazon SageMaker HyperPod training operator helps accelerate generative AI model development by efficiently managing distributed training across large GPU clusters, offering benefits like centralized training process monitoring, granular process recovery, and hanging job detection that can reduce recovery times from tens of minutes to seconds.
How TP ICAP transformed CRM data into real-time insights with Amazon Bedrock
This post shows how TP ICAP used Amazon Bedrock Knowledge Bases and Amazon Bedrock Evaluations to build ClientIQ, an enterprise-grade solution with enhanced security features for extracting CRM insights using AI, delivering immediate business value.
Principal Financial Group accelerates build, test, and deployment of Amazon Lex V2 bots through automation
In the post Principal Financial Group increases Voice Virtual Assistant performance using Genesys, Amazon Lex, and Amazon QuickSight, we discussed the overall Principal Virtual Assistant solution using Genesys Cloud, Amazon Lex V2, multiple AWS services, and a custom reporting and analytics solution using Amazon QuickSight.
Beyond vibes: How to properly select the right LLM for the right task
In this post, we discuss an approach that can guide you to build comprehensive and empirically driven evaluations that can help you make better decisions when selecting the right model for your task.
Splash Music transforms music generation using AWS Trainium and Amazon SageMaker HyperPod
In this post, we show how Splash Music is setting a new standard for AI-powered music creation by using its advanced HummingLM model with AWS Trainium on Amazon SageMaker HyperPod. As a selected startup in the 2024 AWS Generative AI Accelerator, Splash Music collaborated closely with AWS Startups and the AWS Generative AI Innovation Center (GenAIIC) to fast-track innovation and accelerate their music generation FM development lifecycle.
Iterative fine-tuning on Amazon Bedrock for strategic model improvement
Organizations often face challenges when implementing single-shot fine-tuning approaches for their generative AI models. The single-shot fine-tuning method involves selecting training data, configuring hyperparameters, and hoping the results meet expectations without the ability to make incremental adjustments. Single-shot fine-tuning frequently leads to suboptimal results and requires starting the entire process from scratch when improvements are […]
Voice AI-powered drive-thru ordering with Amazon Nova Sonic and dynamic menu displays
In this post, we'll demonstrate how to implement a Quick Service Restaurants (QSRs) drive-thru solution using Amazon Nova Sonic and AWS services. We'll walk through building an intelligent system that combines voice AI with interactive menu displays, providing technical insights and implementation guidance to help restaurants modernize their drive-thru operations.
Optimizing document AI and structured outputs by fine-tuning Amazon Nova Models and on-demand inference
This post provides a comprehensive hands-on guide to fine-tune Amazon Nova Lite for document processing tasks, with a focus on tax form data extraction. Using our open-source GitHub repository code sample, we demonstrate the complete workflow from data preparation to model deployment.
Transforming enterprise operations: Four high-impact use cases with Amazon Nova
In this post, we share four high-impact, widely adopted use cases built with Nova in Amazon Bedrock, supported by real-world customers deployments, offerings available from AWS partners, and experiences. These examples are ideal for organizations researching their own AI adoption strategies and use cases across industries.
Building smarter AI agents: AgentCore long-term memory deep dive
In this post, we explore how Amazon Bedrock AgentCore Memory transforms raw conversational data into persistent, actionable knowledge through sophisticated extraction, consolidation, and retrieval mechanisms that mirror human cognitive processes. The system tackles the complex challenge of building AI agents that don't just store conversations but extract meaningful insights, merge related information across time, and maintain coherent memory stores that enable truly context-aware interactions.
Configure and verify a distributed training cluster with AWS Deep Learning Containers on Amazon EKS
Misconfiguration issues in distributed training with Amazon EKS can be prevented following a systematic approach to launch required components and verify their proper configuration. This post walks through the steps to set up and verify an EKS cluster for training large models using DLCs.
Scala development in Amazon SageMaker Studio with Almond kernel
This post provides a comprehensive guide on integrating the Almond kernel into SageMaker Studio, offering a solution for Scala development within the platform.
Build a device management agent with Amazon Bedrock AgentCore
In this post, we explore how to build a conversational device management system using Amazon Bedrock AgentCore. With this solution, users can manage their IoT devices through natural language, using a UI for tasks like checking device status, configuring WiFi networks, and monitoring user activity.
How Amazon Bedrock Custom Model Import streamlined LLM deployment for Salesforce
This post shows how Salesforce integrated Amazon Bedrock Custom Model Import into their machine learning operations (MLOps) workflow, reused existing endpoints without application changes, and benchmarked scalability. We share key metrics on operational efficiency and cost optimization gains, and offer practical insights for simplifying your deployment strategy.
Transforming the physical world with AI: the next frontier in intelligent automation
In this post, we explore how Physical AI represents the next frontier in intelligent automation, where artificial intelligence transcends digital boundaries to perceive, understand, and manipulate the tangible world around us.
Medical reports analysis dashboard using Amazon Bedrock, LangChain, and Streamlit
In this post, we demonstrate the development of a conceptual Medical Reports Analysis Dashboard that combines Amazon Bedrock AI capabilities, LangChain's document processing, and Streamlit's interactive visualization features. The solution transforms complex medical data into accessible insights through a context-aware chat system powered by large language models available through Amazon Bedrock and dynamic visualizations of health parameters.
Kitsa transforms clinical trial site selection with Amazon Quick Automate
In this post, we'll show how Kitsa, a health-tech company specializing in AI-driven clinical trial recruitment and site selection, used Amazon Quick Automate to transform their clinical trial site selection solution. Amazon Quick Automate, a capability of Amazon Quick Suite, enables enterprises to build, deploy and maintain resilient workflow automations at scale.
Connect Amazon Quick Suite to enterprise apps and agents with MCP
In this post, we explore how Amazon Quick Suite's Model Context Protocol (MCP) client enables secure, standardized connections to enterprise applications and AI agents, eliminating the need for complex custom integrations. You'll discover how to set up MCP Actions integrations with popular enterprise tools like Atlassian Jira and Confluence, AWS Knowledge MCP Server, and Amazon Bedrock AgentCore Gateway to create a collaborative environment where people and AI agents can seamlessly work together across your organization's data and applications.
Make agents a reality with Amazon Bedrock AgentCore: Now generally available
Learn why customers choose AgentCore to build secure, reliable AI solutions using their choice of frameworks and models for production workloads.
Use Amazon SageMaker HyperPod and Anyscale for next-generation distributed computing
In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for distributed AI workloads with high-performance hardware, continuous monitoring, and seamless integration with Ray, the leading AI compute engine, enabling organizations to reduce time-to-market and lower total cost of ownership.
Customizing text content moderation with Amazon Nova
In this post, we introduce Amazon Nova customization for text content moderation through Amazon SageMaker AI, enabling organizations to fine-tune models for their specific moderation needs. The evaluation across three benchmarks shows that customized Nova models achieve an average improvement of 7.3% in F1 scores compared to the baseline Nova Lite, with individual improvements ranging from 4.2% to 9.2% across different content moderation tasks.
Vxceed builds the perfect sales pitch for sales teams at scale using Amazon Bedrock
In this post, we show how Vxceed used Amazon Bedrock to develop this AI-powered multi-agent solution that generates personalized sales pitches for field sales teams at scale.
Implement a secure MLOps platform based on Terraform and GitHub
Machine learning operations (MLOps) is the combination of people, processes, and technology to productionize ML use cases efficiently. To achieve this, enterprise customers must develop MLOps platforms to support reproducibility, robustness, and end-to-end observability of the ML use case’s lifecycle. Those platforms are based on a multi-account setup by adopting strict security constraints, development best […]
Automate Amazon QuickSight data stories creation with agentic AI using Amazon Nova Act
In this post, we demonstrate how Amazon Nova Act automates QuickSight data story creation, saving time so you can focus on making critical, data-driven business decisions.
Implement automated monitoring for Amazon Bedrock batch inference
In this post, we demonstrated how a financial services company can use an FM to process large volumes of customer records and get specific data-driven product recommendations. We also showed how to implement an automated monitoring solution for Amazon Bedrock batch inference jobs. By using EventBridge, Lambda, and DynamoDB, you can gain real-time visibility into batch processing operations, so you can efficiently generate personalized product recommendations based on customer credit data.
Responsible AI: How PowerSchool safeguards millions of students with AI-powered content filtering using Amazon SageMaker AI
In this post, we demonstrate how PowerSchool built and deployed a custom content filtering solution using Amazon SageMaker AI that achieved better accuracy while maintaining low false positive rates. We walk through our technical approach to fine tuning Llama 3.1 8B, our deployment architecture, and the performance results from internal validations.
Unlock global AI inference scalability using new global cross-Region inference on Amazon Bedrock with Anthropic’s Claude Sonnet 4.5
Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline operations, and drive innovation. As generative AI workloads continue to grow in scale and importance, organizations face new challenges in maintaining consistent performance, reliability, and availability of their AI-powered applications. Customers are looking to scale their AI inference workloads across […]
Secure ingress connectivity to Amazon Bedrock AgentCore Gateway using interface VPC endpoints
In this post, we demonstrate how to access AgentCore Gateway through a VPC interface endpoint from an Amazon Elastic Compute Cloud (Amazon EC2) instance in a VPC. We also show how to configure your VPC endpoint policy to provide secure access to the AgentCore Gateway while maintaining the principle of least privilege access.
Enhance agentic workflows with enterprise search using Kore.ai and Amazon Q Business
In this post, we demonstrate how organizations can enhance their employee productivity by integrating Kore.ai’s AI for Work platform with Amazon Q Business. We show how to configure AI for Work as a data accessor for Amazon Q index for independent software vendors (ISVs), so employees can search enterprise knowledge and execute end-to-end agentic workflows involving search, reasoning, actions, and content generation.
Accelerate development with the Amazon Bedrock AgentCore MCP server
Today, we’re excited to announce the Amazon Bedrock AgentCore Model Context Protocol (MCP) Server. With built-in support for runtime, gateway integration, identity management, and agent memory, the AgentCore MCP Server is purpose-built to speed up creation of components compatible with Bedrock AgentCore. You can use the AgentCore MCP server for rapid prototyping, production AI solutions, […]
Rox accelerates sales productivity with AI agents powered by Amazon Bedrock
We’re excited to announce that Rox is generally available, with Rox infrastructure built on AWS and delivered across web, Slack, macOS, and iOS. In this post, we share how Rox accelerates sales productivity with AI agents powered by Amazon Bedrock.
Modernize fraud prevention: GraphStorm v0.5 for real-time inference
In this post, we demonstrate how to implement real-time fraud prevention using GraphStorm v0.5's new capabilities for deploying graph neural network (GNN) models through Amazon SageMaker. We show how to transition from model training to production-ready inference endpoints with minimal operational overhead, enabling sub-second fraud detection on transaction graphs with billions of nodes and edges.
Building health care agents using Amazon Bedrock AgentCore
In this solution, we demonstrate how the user (a parent) can interact with a Strands or LangGraph agent in conversational style and get information about the immunization history and schedule of their child, inquire about the available slots, and book appointments. With some changes, AI agents can be made event-driven so that they can automatically send reminders, book appointments, and so on.
Build multi-agent site reliability engineering assistants with Amazon Bedrock AgentCore
In this post, we demonstrate how to build a multi-agent SRE assistant using Amazon Bedrock AgentCore, LangGraph, and the Model Context Protocol (MCP). This system deploys specialized AI agents that collaborate to provide the deep, contextual intelligence that modern SRE teams need for effective incident response and infrastructure management.
DoWhile loops now supported in Amazon Bedrock Flows
Today, we are excited to announce support for DoWhile loops in Amazon Bedrock Flows. With this powerful new capability, you can create iterative, condition-based workflows directly within your Amazon Bedrock flows, using Prompt nodes, AWS Lambda functions, Amazon Bedrock Agents, Amazon Bedrock Flows inline code, Amazon Bedrock Knowledge Bases, Amazon Simple Storage Service (Amazon S3), […]
How PropHero built an intelligent property investment advisor with continuous evaluation using Amazon Bedrock
In this post, we explore how we built a multi-agent conversational AI system using Amazon Bedrock that delivers knowledge-grounded property investment advice. We explore the agent architecture, model selection strategy, and comprehensive continuous evaluation system that facilitates quality conversations while facilitating rapid iteration and improvement.
Accelerate benefits claims processing with Amazon Bedrock Data Automation
In the benefits administration industry, claims processing is a vital operational pillar that makes sure employees and beneficiaries receive timely benefits, such as health, dental, or disability payments, while controlling costs and adhering to regulations like HIPAA and ERISA. In this post, we examine the typical benefit claims processing workflow and identify where generative AI-powered automation can deliver the greatest impact.
Running deep research AI agents on Amazon Bedrock AgentCore
AI agents are evolving beyond basic single-task helpers into more powerful systems that can plan, critique, and collaborate with other agents to solve complex problems. Deep Agents—a recently introduced framework built on LangGraph—bring these capabilities to life, enabling multi-agent workflows that mirror real-world team dynamics. The challenge, however, is not just building such agents but […]
Integrate tokenization with Amazon Bedrock Guardrails for secure data handling
In this post, we show you how to integrate Amazon Bedrock Guardrails with third-party tokenization services to protect sensitive data while maintaining data reversibility. By combining these technologies, organizations can implement stronger privacy controls while preserving the functionality of their generative AI applications and related systems.
Rapid ML experimentation for enterprises with Amazon SageMaker AI and Comet
In this post, we showed how to use SageMaker and Comet together to spin up fully managed ML environments with reproducibility and experiment tracking capabilities.
Move your AI agents from proof of concept to production with Amazon Bedrock AgentCore
This post explores how Amazon Bedrock AgentCore helps you transition your agentic applications from experimental proof of concept to production-ready systems. We follow the journey of a customer support agent that evolves from a simple local prototype to a comprehensive, enterprise-grade solution capable of handling multiple concurrent users while maintaining security and performance standards.
Scale visual production using Stability AI Image Services in Amazon Bedrock
This post was written with Alex Gnibus of Stability AI. Stability AI Image Services are now available in Amazon Bedrock, offering ready-to-use media editing capabilities delivered through the Amazon Bedrock API. These image editing tools expand on the capabilities of Stability AI’s Stable Diffusion 3.5 models (SD3.5) and Stable Image Core and Ultra models, which […]
Prompting for precision with Stability AI Image Services in Amazon Bedrock
Amazon Bedrock now offers Stability AI Image Services: 9 tools that improve how businesses create and modify images. The technology extends Stable Diffusion and Stable Image models to give you precise control over image creation and editing. Clear prompts are critical—they provide art direction to the AI system. Strong prompts control specific elements like tone, […]
Monitor Amazon Bedrock batch inference using Amazon CloudWatch metrics
In this post, we explore how to monitor and manage Amazon Bedrock batch inference jobs using Amazon CloudWatch metrics, alarms, and dashboards to optimize performance, cost, and operational efficiency.
Use AWS Deep Learning Containers with Amazon SageMaker AI managed MLflow
In this post, we show how to integrate AWS DLCs with MLflow to create a solution that balances infrastructure control with robust ML governance. We walk through a functional setup that your team can use to meet your specialized requirements while significantly reducing the time and resources needed for ML lifecycle management.
Supercharge your organization’s productivity with the Amazon Q Business browser extension
In this post, we showed how to use the Amazon Q Business browser extension to give your team seamless access to AI-driven insights and assistance. The browser extension is now available in US East (N. Virginia) and US West (Oregon) AWS Regions for Mozilla, Google Chrome, and Microsoft Edge as part of the Lite Subscription.
Build Agentic Workflows with OpenAI GPT OSS on Amazon SageMaker AI and Amazon Bedrock AgentCore
In this post, we show how to deploy gpt-oss-20b model to SageMaker managed endpoints and demonstrate a practical stock analyzer agent assistant example with LangGraph, a powerful graph-based framework that handles state management, coordinated workflows, and persistent memory systems.
Streamline access to ISO-rating content changes with Verisk rating insights and Amazon Bedrock
In this post, we dive into how Verisk Rating Insights, powered by Amazon Bedrock, large language models (LLM), and Retrieval Augmented Generation (RAG), is transforming the way customers interact with and access ISO ERC changes.
Unified multimodal access layer for Quora’s Poe using Amazon Bedrock
In this post, we explore how the AWS Generative AI Innovation Center and Quora collaborated to build a unified wrapper API framework that dramatically accelerates the deployment of Amazon Bedrock FMs on Quora’s Poe system. We detail the technical architecture that bridges Poe’s event-driven ServerSentEvents protocol with Amazon Bedrock REST-based APIs, demonstrate how a template-based configuration system reduced deployment time from days to 15 minutes, and share implementation patterns for protocol translation, error handling, and multi-modal capabilities.
Schedule topology-aware workloads using Amazon SageMaker HyperPod task governance
In this post, we introduce topology-aware scheduling with SageMaker HyperPod task governance by submitting jobs that represent hierarchical network information. We provide details about how to use SageMaker HyperPod task governance to optimize your job efficiency.
How msg enhanced HR workforce transformation with Amazon Bedrock and msg.ProfileMap
In this post, we share how msg automated data harmonization for msg.ProfileMap, using Amazon Bedrock to power its large language model (LLM)-driven data enrichment workflows, resulting in higher accuracy in HR concept matching, reduced manual workload, and improved alignment with compliance requirements under the EU AI Act and GDPR.
Automate advanced agentic RAG pipeline with Amazon SageMaker AI
In this post, we walk through how to streamline your RAG development lifecycle from experimentation to automation, helping you operationalize your RAG solution for production deployments with Amazon SageMaker AI, helping your team experiment efficiently, collaborate effectively, and drive continuous improvement.
Unlock model insights with log probability support for Amazon Bedrock Custom Model Import
In this post, we explore how log probabilities work with imported models in Amazon Bedrock. You will learn what log probabilities are, how to enable them in your API calls, and how to interpret the returned data. We also highlight practical applications—from detecting potential hallucinations to optimizing RAG systems and evaluating fine-tuned models—that demonstrate how these insights can improve your AI applications, helping you build more trustworthy solutions with your custom models.
Migrate from Anthropic’s Claude 3.5 Sonnet to Claude 4 Sonnet on Amazon Bedrock
This post provides a systematic approach to migrating from Anthropic’s Claude 3.5 Sonnet to Claude 4 Sonnet on Amazon Bedrock. We examine the key model differences, highlight essential migration considerations, and deliver proven best practices to transform this necessary transition into a strategic advantage that drives measurable value for your organization.
Enhance video understanding with Amazon Bedrock Data Automation and open-set object detection
In real-world video and image analysis, businesses often face the challenge of detecting objects that weren’t part of a model’s original training set. This becomes especially difficult in dynamic environments where new, unknown, or user-defined objects frequently appear. In this post, we explore how Amazon Bedrock Data Automation uses OSOD to enhance video understanding.
How Skello uses Amazon Bedrock to query data in a multi-tenant environment while keeping logical boundaries
Skello is a leading human resources (HR) software as a service (SaaS) solution focusing on employee scheduling and workforce management. Catering to diverse sectors such as hospitality, retail, healthcare, construction, and industry, Skello offers features including schedule creation, time tracking, and payroll preparation. We dive deep into the challenges of implementing large language models (LLMs) for data querying, particularly in the context of a French company operating under the General Data Protection Regulation (GDPR).
Create a private workforce on Amazon SageMaker Ground Truth with the AWS CDK
In this post, we present a complete solution for programmatically creating private workforces on Amazon SageMaker AI using the AWS Cloud Development Kit (AWS CDK), including the setup of a dedicated, fully configured Amazon Cognito user pool.
TII Falcon-H1 models now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart
We are excited to announce the availability of the Technology Innovation Institute (TII)’s Falcon-H1 models on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, developers and data scientists can now use six instruction-tuned Falcon-H1 models (0.5B, 1.5B, 1.5B-Deep, 3B, 7B, and 34B) on AWS, and have access to a comprehensive suite of hybrid architecture models that combine traditional attention mechanisms with State Space Models (SSMs) to deliver exceptional performance with unprecedented efficiency.
Oldcastle accelerates document processing with Amazon Bedrock
This post explores how Oldcastle partnered with AWS to transform their document processing workflow using Amazon Bedrock with Amazon Textract. We discuss how Oldcastle overcame the limitations of their previous OCR solution to automate the processing of hundreds of thousands of POD documents each month, dramatically improving accuracy while reducing manual effort.
How London Stock Exchange Group is detecting market abuse with their AI-powered Surveillance Guide on Amazon Bedrock
In this post, we explore how London Stock Exchange Group (LSEG) used Amazon Bedrock and Anthropic's Claude foundation models to build an automated system that significantly improves the efficiency and accuracy of market surveillance operations.
Build trustworthy AI agents with Amazon Bedrock AgentCore Observability
In this post, we walk you through implementation options for both agents hosted on Amazon Bedrock AgentCore Runtime and agents hosted on other services like Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Kubernetes Service (Amazon EKS), AWS Lambda, or alternative cloud providers. We also share best practices for incorporating observability throughout the development lifecycle.
Powering innovation at scale: How AWS is tackling AI infrastructure challenges
As generative AI continues to transform how enterprises operate—and develop net new innovations—the infrastructure demands for training and deploying AI models have grown exponentially. Traditional infrastructure approaches are struggling to keep pace with today’s computational requirements, network demands, and resilience needs of modern AI workloads. At AWS, we’re also seeing a transformation across the technology […]
Accelerate your model training with managed tiered checkpointing on Amazon SageMaker HyperPod
AWS announced managed tiered checkpointing in Amazon SageMaker HyperPod, a purpose-built infrastructure to scale and accelerate generative AI model development across thousands of AI accelerators. Managed tiered checkpointing uses CPU memory for high-performance checkpoint storage with automatic data replication across adjacent compute nodes for enhanced reliability. In this post, we dive deep into those concepts and understand how to use the managed tiered checkpointing feature.
Maximize HyperPod Cluster utilization with HyperPod task governance fine-grained quota allocation
We are excited to announce the general availability of fine-grained compute and memory quota allocation with HyperPod task governance. With this capability, customers can optimize Amazon SageMaker HyperPod cluster utilization on Amazon Elastic Kubernetes Service (Amazon EKS), distribute fair usage, and support efficient resource allocation across different teams or projects. For more information, see HyperPod task governance best […]
Build and scale adoption of AI agents for education with Strands Agents, Amazon Bedrock AgentCore, and LibreChat
This post demonstrates how to quickly build sophisticated AI agents using Strands Agents, scale them reliably with Amazon Bedrock AgentCore, and make them accessible through LibreChat’s familiar interface to drive immediate user adoption across your institution.
Skai uses Amazon Bedrock Agents to significantly improve customer insights by revolutionized data access and analysis
Skai (formerly Kenshoo) is an AI-driven omnichannel advertising and analytics platform designed for brands and agencies to plan, launch, optimize, and measure paid media across search, social, retail media marketplaces and other “walled-garden” channels from a single interface. In this post, we share how Skai used Amazon Bedrock Agents to improve data access and analysis and improve customer insights.
The power of AI in driving personalized product discovery at Snoonu
In this post, we share how Snoonu, a leading ecommerce platform in the Middle East, transformed their product discovery experience using AI-powered personalization. In this post, we share how Snoonu, a leading ecommerce platform in the Middle East, transformed their product discovery experience using AI-powered personalization.
Accelerating HPC and AI research in universities with Amazon SageMaker HyperPod
In this post, we demonstrate how a research university implemented SageMaker HyperPod to accelerate AI research by using dynamic SLURM partitions, fine-grained GPU resource management, budget-aware compute cost tracking, and multi-login node load balancing—all integrated seamlessly into the SageMaker HyperPod environment.
Exploring the Real-Time Race Track with Amazon Nova
This post explores the Real-Time Race Track (RTRT), an interactive experience built using Amazon Nova in Amazon Bedrock, that lets fans design, customize, and share their own racing circuits. We highlight how generative AI capabilities come together to deliver strategic racing insights such as pit timing and tire choices, and interactive features like an AI voice assistant and a retro-style racing poster.
Build character consistent storyboards using Amazon Nova in Amazon Bedrock – Part 2
In this post, we take an animated short film, Picchu, produced by FuzzyPixel from Amazon Web Services (AWS), prepare training data by extracting key character frames, and fine-tune a character-consistent model for the main character Mayu and her mother, so we can quickly generate storyboard concepts for new sequels like the following images.
Build character consistent storyboards using Amazon Nova in Amazon Bedrock – Part 1
The art of storyboarding stands as the cornerstone of modern content creation, weaving its essential role through filmmaking, animation, advertising, and UX design. Though traditionally, creators have relied on hand-drawn sequential illustrations to map their narratives, today’s AI foundation models (FMs) are transforming this landscape. FMs like Amazon Nova Canvas and Amazon Nova Reel offer […]
Authenticate Amazon Q Business data accessors using a trusted token issuer
In this post, we showed how to implement TTI authentication for Amazon Q data accessors. We covered the setup process for both ISVs and enterprises and demonstrated how TTI authentication simplifies the user experience while maintaining security standards.
Unlocking the future of professional services: How Proofpoint uses Amazon Q Business
Proofpoint has redefined its professional services by integrating Amazon Q Business, a fully managed, generative AI powered assistant that you can configure to answer questions, provide summaries, generate content, and complete tasks based on your enterprise data. In this post, we explore how Amazon Q Business transformed Proofpoint’s professional services, detailing its deployment, functionality, and future roadmap.
Enhancing LLM accuracy with Coveo Passage Retrieval on Amazon Bedrock
In this post, we show how to deploy Coveo’s Passage Retrieval API as an Amazon Bedrock Agents action group to enhance response accuracy, so Coveo users can use their current index to rapidly deploy new generative experiences across their organization.
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.