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

AWS Machine Learning Blog

The custom metadata helps organizations and enterprises categorize information in their preferred way. The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. Custom classification is a two-step process.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

These generative AI applications are not only used to automate existing business processes, but also have the ability to transform the experience for customers using these applications. To address these challenges, parent document retrievers categorize and designate incoming documents as parent documents.

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Segment Anything Model (SAM) Deep Dive – Complete 2024 Guide

Viso.ai

Today, the computer vision project has gained enormous momentum in mobile applications, automated image annotation tools , and facial recognition and image classification applications. In retail , SAM could revolutionize inventory management through automated product recognition and categorization.

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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab. Cost-sensitive classification – In some applications, the cost of misclassification for different classes can be different.

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How to Build ML Model Training Pipeline

The MLOps Blog

Complete ML model training pipeline workflow | Source But before we delve into the step-by-step model training pipeline, it’s essential to understand the basics, architecture, motivations, challenges associated with ML pipelines, and a few tools that you will need to work with. Let’s get started! Define the preprocessing steps.

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Use foundation models to improve model accuracy with Amazon SageMaker

AWS Machine Learning Blog

We propose using this capability with the Amazon SageMaker platform of services to improve regression model accuracy in an ML use case, and independently, for the automated tagging of visual images. Answers can come in the form of categorical, continuous value, or binary responses. granite, tile, marble, laminate, etc.

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Fine-tune Llama 2 for text generation on Amazon SageMaker JumpStart

AWS Machine Learning Blog

What is Llama 2 Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Write a response that appropriately completes the request.nn### Instruction:nWhen did Felix Luna die?nn### Write a response that appropriately completes the request.nn### Instruction:nWhat is an egg laying mammal?nn###