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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

AWS Machine Learning - AI

National Laboratory has implemented an AI-driven document processing platform that integrates named entity recognition (NER) and large language models (LLMs) on Amazon SageMaker AI. In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

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Unbundling the Graph in GraphRAG

O'Reilly Media - Ideas

Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. Also, in place of expensive retraining or fine-tuning for an LLM, this approach allows for quick data updates at low cost.

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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning - AI

Amazon SageMaker HyperPod resilient training infrastructure SageMaker HyperPod is a compute environment optimized for large-scale frontier model training. Frontier model builders can further enhance model performance using built-in ML tools within SageMaker HyperPod.

Training 101
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Foundation Model for Personalized Recommendation

Netflix Tech

By Ko-Jen Hsiao , Yesu Feng and Sudarshan Lamkhede Motivation Netflixs personalized recommender system is a complex system, boasting a variety of specialized machine learned models each catering to distinct needs including Continue Watching and Todays Top Picks for You.

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Creating asynchronous AI agents with Amazon Bedrock

AWS Machine Learning - AI

Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. This post will discuss agentic AI driven architecture and ways of implementing.

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AI agents loom large as organizations pursue generative AI value

CIO

In such systems, multiple agents execute tasks intended to achieve an overarching goal, such as automating payroll, HR processes, and even software development, based on text, images, audio, and video from large language models (LLMs). A similar approach to infrastructure can help.

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Beyond AI: Building toward artificial consciousness – Part 2

CIO

Beyond the hype surrounding artificial intelligence (AI) in the enterprise lies the next step—artificial consciousness. The first piece in this practical AI innovation series outlined the requirements for this technology , which delved deeply into compute power—the core capability necessary to enable artificial consciousness.