AI

Supercharge your organization’s productivity with the Amazon Q Business browser extension

Generative AI solutions like Amazon Q Business are transforming the way employees work. Organizations in every industry are embracing these tools to help their workforce extract valuable insights from increasingly fragmented data to accelerate decision-making processes. However, the adoption of generative AI tools hasn’t been without its challenges. Two hurdles have emerged in the implementation […]

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Build Agentic Workflows with OpenAI GPT OSS on Amazon SageMaker AI and Amazon Bedrock AgentCore

OpenAI has released two open-weight models, gpt-oss-120b (117 billion parameters) and gpt-oss-20b (21 billion parameters), both built with a Mixture of Experts (MoE) design and a 128K context window. These models are the leading open source models, according to Artificial Analysis benchmarks, and excel at reasoning and agentic workflows. With Amazon SageMaker AI, you can

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Streamline access to ISO-rating content changes with Verisk rating insights and Amazon Bedrock

This post is co-written with Samit Verma, Eusha Rizvi, Manmeet Singh, Troy Smith, and Corey Finley from Verisk. Verisk Rating Insights as a feature of ISO Electronic Rating Content (ERC) is a powerful tool designed to provide summaries of ISO Rating changes between two releases. Traditionally, extracting specific filing information or identifying differences across multiple

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Unified multimodal access layer for Quora’s Poe using Amazon Bedrock

Organizations gain competitive advantage by deploying and integrating new generative AI models quickly through Generative AI Gateway architectures. This unified interface approach simplifies access to multiple foundation models (FMs), addressing a critical challenge: the proliferation of specialized AI models, each with unique capabilities, API specifications, and operational requirements. Rather than building and maintaining separate integration

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How to build AI scaling laws for efficient LLM training and budget maximization

When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate

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