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

Flipboard

Traditional keyword-based search mechanisms are often insufficient for locating relevant documents efficiently, requiring extensive manual review to extract meaningful insights. This solution improves the findability and accessibility of archival records by automating metadata enrichment, document classification, and summarization.

AWS 104
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Understanding REST API: A comprehensive guide

Data Science Dojo

Layered System: REST API should be designed in a layered system architecture, where each layer has a specific role and responsibility. The layered system architecture helps to promote scalability, reliability, and flexibility. The uniform interface helps to simplify the API and promotes reusability.

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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

For many of these use cases, businesses are building Retrieval Augmented Generation (RAG) style chat-based assistants, where a powerful LLM can reference company-specific documents to answer questions relevant to a particular business or use case. Generate a grounded response to the original question based on the retrieved documents.

AWS 130
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

The traditional approach of manually sifting through countless research documents, industry reports, and financial statements is not only time-consuming but can also lead to missed opportunities and incomplete analysis. This event-driven architecture provides immediate processing of new documents.

AWS 117
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Killswitch engineer at OpenAI: A role under debate

Dataconomy

Understanding system architecture A killswitch engineer at OpenAI would be responsible for more than just pulling a plug. The role necessitates a deep understanding of system architecture, including the layers of hardware and software that run AI models like upcoming GPT-5.

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Unbundling the Graph in GraphRAG

O'Reilly Media

Here’s a simple rough sketch of RAG: Start with a collection of documents about a domain. Split each document into chunks. One more embellishment is to use a graph neural network (GNN) trained on the documents. Chunk your documents from unstructured data sources, as usual in GraphRAG. at Facebook—both from 2020.

Database 127
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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

To generate a useful response, the chat would need to reference different data sources, including the unstructured documents in your knowledge base (such as policy documentation about what causes an account suspension) and structured data such as transaction history and real-time account activity.

ML 102