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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

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How Memorial Sloan Kettering Cancer Center (MSKCC) used Snorkel Flow to scale clinical trial screening

Snorkel AI

Scaling clinical trial screening with document classification Memorial Sloan Kettering Cancer Center, the world’s oldest and largest private cancer center, provides care to increase the quality of life of more than 150,000 cancer patients annually. Watch this and many other sessions on-demand at future.snorkel.ai.

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Understanding Graph Neural Network with hands-on example| Part-1

Becoming Human

A typical application of GNN is node classification. The problems that GNNs are used to solve can be divided into the following categories: Node Classification: The goal of this task is to determine the labeling of samples (represented as nodes) by examining the labels of their immediate neighbors (i.e., their neighbors’ labels).

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

If you’re implementing complex RAG applications into your daily tasks, you may encounter common challenges with your RAG systems such as inaccurate retrieval, increasing size and complexity of documents, and overflow of context, which can significantly impact the quality and reliability of generated answers.

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Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

AWS Machine Learning Blog

An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language processing (NLP) to read and understand a document and extract specific terms or words. This can be achieved by updating the endpoint’s inference units (IUs).

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Revolutionizing Autonomy: CNNs in Self-Driving Cars

Towards AI

The Advanced Driver Assistance System (ADAS) is a sis-tiered system that categorizes the different levels of autonomy. A CNN is a neural network with one or more convolutional layers and is used mainly for image processing, classification, segmentation, and other auto-correlated data. Levels of Autonomy. [3] Yann LeCun et al.,

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Hosting ML Models on Amazon SageMaker using Triton: XGBoost, LightGBM, and Treelite Models

AWS Machine Learning Blog

With the ability to solve various problems such as classification and regression, XGBoost has become a popular option that also falls into the category of tree-based models. These models have long been used for solving problems such as classification or regression. threshold – This is a score threshold for determining classification.

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