AI

Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI

Foundation model (FM) training and inference has led to a significant increase in computational needs across the industry. These models require massive amounts of accelerated compute to train and operate effectively, pushing the boundaries of traditional computing infrastructure. They require efficient systems for distributing workloads across multiple GPU accelerated servers, and optimizing developer velocity as […]

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Using Large Language Models on Amazon Bedrock for multi-step task execution

The goal of this blog post is to show you how a large language model (LLM) can be used to perform tasks that require multi-step dynamic reasoning and execution. Examples of tasks that require dynamic reasoning and execution are answering questions of the form “What is the average length of stay for patients with [specific

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Researchers teach LLMs to solve complex planning challenges

Imagine a coffee company trying to optimize its supply chain. The company sources beans from three suppliers, roasts them at two facilities into either dark or light coffee, and then ships the roasted coffee to three retail locations. The suppliers have different fixed capacity, and roasting costs and shipping costs vary from place to place.

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Introducing AWS MCP Servers for code assistants (Part 1)

We’re excited to announce the open source release of AWS MCP Servers for code assistants — a suite of specialized Model Context Protocol (MCP) servers that bring Amazon Web Services (AWS) best practices directly to your development workflow. Our specialized AWS MCP servers combine deep AWS knowledge with agentic AI capabilities to accelerate development across

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Harness the power of MCP servers with Amazon Bedrock Agents

AI agents extend large language models (LLMs) by interacting with external systems, executing complex workflows, and maintaining contextual awareness across operations. Amazon Bedrock Agents enables this functionality by orchestrating foundation models (FMs) with data sources, applications, and user inputs to complete goal-oriented tasks through API integration and knowledge base augmentation. However, in the past, connecting

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Generate compliant content with Amazon Bedrock and ConstitutionalChain

Generative AI has emerged as a powerful tool for content creation, offering several key benefits that can significantly enhance the efficiency and effectiveness of content production processes such as creating marketing materials, image generation, content moderation etc. Constitutional AI and LangGraph‘s reflection mechanisms represent two complementary approaches to ensuring AI systems behave ethically – with

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Minimize generative AI hallucinations with Amazon Bedrock Automated Reasoning checks

Foundation models (FMs) and generative AI are transforming enterprise operations across industries. McKinsey & Company’s recent research estimates generative AI could contribute up to $4.4 trillion annually to the global economy through enhanced operational efficiency, productivity growth of 0.1% to 0.6% annually, improved customer experience through personalized interactions, and accelerated digital transformation. Today, organizations struggle

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AWS App Studio introduces a prebuilt solutions catalog and cross-instance Import and Export

AWS App Studio is a generative AI-powered service that uses natural language to build business applications, empowering a new set of builders to create applications in minutes. With App Studio, technical professionals such as IT project managers, data engineers, enterprise architects, and solution architects can quickly develop applications tailored to their organization’s needs—without requiring deep

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