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Intelligent document processing with AWS AI and Analytics services in the insurance industry: Part 2

AWS Machine Learning

We also discussed how to extract various types of documents in an insurance claims package, such as forms, tables, or specialized documents such as invoices, receipts, or ID documents. We discussed how we can use AWS AI services to accurately categorize claims documents along with supporting documents. Extraction phase. client('comprehend').

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Intelligent document processing with AWS AI services: Part 2

AWS Machine Learning

Because of the varied formats, most firms manually process documents such as W2s, claims, ID documents, invoices, and legal contracts, or use legacy OCR (optical character recognition) solutions that are time-consuming, error-prone, and costly. She is passionate about applied mathematics and machine learning. About the authors.

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Zero-shot and few-shot prompting for the BloomZ 176B foundation model with the simplified Amazon SageMaker JumpStart SDK

AWS Machine Learning

Amazon SageMaker JumpStart is a machine learning (ML) hub offering algorithms, models, and ML solutions. The BloomZ 176B model, one of the largest publicly available models, is a state-of-the-art instruction-tuned model that can perform various in-context few-shot learning and zero-shot learning NLP tasks.

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Intelligent document processing with AWS AI services: Part 1

AWS Machine Learning

Intelligent document processing (IDP) with AWS artificial intelligence (AI) services helps automate information extraction from documents of different types and formats, quickly and with high accuracy, without the need for machine learning (ML) skills. Invoices and receipts. Deploy a real-time endpoint.

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Encode multi-lingual text properties in Amazon Neptune to train predictive models

AWS Machine Learning

Amazon Neptune ML is a machine learning (ML) capability of Amazon Neptune that helps you make accurate and fast predictions on your graph data. FastText is a library for efficient text representation learning. SBERT is a kind of sentence embedding method using the contextual representation learning models, BERT-Networks.