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Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio

AWS Machine Learning - AI

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. Building a generative AI application SageMaker Unified Studio offers tools to discover and build with generative AI.

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Unleash the power of generative AI with Amazon Q Business: How CCoEs can scale cloud governance best practices and drive innovation

AWS Machine Learning - AI

In this post, we share how Hearst , one of the nation’s largest global, diversified information, services, and media companies, overcame these challenges by creating a self-service generative AI conversational assistant for business units seeking guidance from their CCoE.

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Supercharge your auto scaling for generative AI inference – Introducing Container Caching in SageMaker Inference

AWS Machine Learning - AI

Today at AWS re:Invent 2024, we are excited to announce the new Container Caching capability in Amazon SageMaker, which significantly reduces the time required to scale generative AI models for inference. In our tests, we’ve seen substantial improvements in scaling times for generative AI model endpoints across various frameworks.

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EBSCOlearning scales assessment generation for their online learning content with generative AI

AWS Machine Learning - AI

In this post, we illustrate how EBSCOlearning partnered with AWS Generative AI Innovation Center (GenAIIC) to use the power of generative AI in revolutionizing their learning assessment process. Sonnet in Amazon Bedrock.

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Digital transformation 2025: What’s in, what’s out

CIO

Transformational CIOs continuously invest in their operating model by developing product management, design thinking, agile, DevOps, change management, and data-driven practices. 2025 will be the year when generative AI needs to generate value, says Louis Landry, CTO at Teradata.

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Improving Retrieval Augmented Generation accuracy with GraphRAG

AWS Machine Learning - AI

Customers need better accuracy to take generative AI applications into production. This enhancement is achieved by using the graphs ability to model complex relationships and dependencies between data points, providing a more nuanced and contextually accurate foundation for generative AI outputs.

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LexisNexis rises to the generative AI challenge

CIO

IT leaders looking for a blueprint for staving off the disruptive threat of generative AI might benefit from a tip from LexisNexis EVP and CTO Jeff Reihl: Be a fast mover in adopting the technology to get ahead of potential disruptors. This is where some of our initial work with AI started,” Reihl says. “We We use AWS and Azure.