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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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Global AI regulations: Beyond the U.S. and Europe

CIO

Second, some countries such as the United Arab Emirates (UAE) have implemented sector-specific AI requirements while allowing other sectors to follow voluntary guidelines. The G7 collection of nations has also proposed a voluntary AI code of conduct. Similar voluntary guidance can be seen in Singapore and Japan.

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Introducing AWS MCP Servers for code assistants (Part 1)

AWS Machine Learning - AI

Were 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. Developers need code assistants that understand the nuances of AWS services and best practices.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning - AI

In this post, we seek to address this growing need by offering clear, actionable guidelines and best practices on when to use each approach, helping you make informed decisions that align with your unique requirements and objectives. On the Review and create page, review the settings and choose Create Knowledge Base.

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AI Strategies for Software Engineering Career Growth

Honeycomb

All the conditions necessary to alter the career paths of brand new software engineers coalescedextreme layoffs and hiring freezes in tech danced with the irreversible introduction of ChatGPT and GitHub Copilot. toggling settings so the bot wont learn from our convos at Honeycomb).

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Using John Snow Labs’ Medical Large Language Models on Azure Fabric

John Snow Labs

John Snow Labs’ Medical Language Models library is an excellent choice for leveraging the power of large language models (LLM) and natural language processing (NLP) in Azure Fabric due to its seamless integration, scalability, and state-of-the-art accuracy on medical tasks.

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.