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Build scalable creative solutions for product teams with Amazon Bedrock
In this post, we explore how product teams can leverage Amazon Bedrock and AWS services to transform their creative workflows through generative AI, enabling rapid content iteration across multiple formats while maintaining brand consistency and compliance. The solution demonstrates how teams can deploy a scalable generative AI application that accelerates everything from product descriptions and marketing copy to visual concepts and video content, significantly reducing time to market while enhancing creative quality.
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Build a proactive AI cost management system for Amazon Bedrock – Part 2
In this post, we explore advanced cost monitoring strategies for Amazon Bedrock deployments, introducing granular custom tagging approaches for precise cost allocation and comprehensive reporting mechanisms that build upon the proactive cost management foundation established in Part 1. The solution demonstrates how to implement invocation-level tagging, application inference profiles, and integration with AWS Cost Explorer to create a complete 360-degree view of generative AI usage and expenses.
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Build a proactive AI cost management system for Amazon Bedrock – Part 1
In this post, we introduce a comprehensive solution for proactively managing Amazon Bedrock inference costs through a cost sentry mechanism designed to establish and enforce token usage limits, providing organizations with a robust framework for controlling generative AI expenses. The solution uses serverless workflows and native Amazon Bedrock integration to deliver a predictable, cost-effective approach that aligns with organizational financial constraints while preventing runaway costs through leading indicators and real-time budget enforcement.
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Streamline code migration using Amazon Nova Premier with an agentic workflow
In this post, we demonstrate how Amazon Nova Premier with Amazon Bedrock can systematically migrate legacy C code to modern Java/Spring applications using an intelligent agentic workflow that breaks down complex conversions into specialized agent roles. The solution reduces migration time and costs while improving code quality through automated validation, security assessment, and iterative refinement processes that handle even large codebases exceeding token limitations.
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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 .
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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 .
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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, […]
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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.
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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.
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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), […]
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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.
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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.
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