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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. Although an individual LLM can be highly capable, it might not optimally address a wide range of use cases or meet diverse performance requirements.

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Extend large language models powered by Amazon SageMaker AI using Model Context Protocol

AWS Machine Learning - AI

For MCP implementation, you need a scalable infrastructure to host these servers and an infrastructure to host the large language model (LLM), which will perform actions with the tools implemented by the MCP server. You ask the agent to Book a 5-day trip to Europe in January and we like warm weather.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

Generative artificial intelligence ( genAI ) and in particular large language models ( LLMs ) are changing the way companies develop and deliver software. These autoregressive models can ultimately process anything that can be easily broken down into tokens: image, video, sound and even proteins.

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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

The emergence of generative AI has ushered in a new era of possibilities, enabling the creation of human-like text, images, code, and more. Solution overview For this solution, you deploy a demo application that provides a clean and intuitive UI for interacting with a generative AI model, as illustrated in the following screenshot.

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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. 70B model showed significant and consistent improvements in end-to-end (E2E) scaling times.

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Dulling the impact of AI-fueled cyber threats with AI

CIO

IT leaders are placing faith in AI. Consider 76 percent of IT leaders believe that generative AI (GenAI) will significantly impact their organizations, with 76 percent increasing their budgets to pursue AI. But when it comes to cybersecurity, AI has become a double-edged sword.

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The key to operational AI: Modern data architecture

CIO

Today, enterprises are leveraging various types of AI to achieve their goals. Just as DevOps has become an effective model for organizing application teams, a similar approach can be applied here through machine learning operations, or “MLOps,” which automates machine learning workflows and deployments.