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Harmonize data using AWS Glue and AWS Lake Formation FindMatches ML to build a customer 360 view

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In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience. The following diagram shows our solution architecture.

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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

Use case overview The use case outlined in this post is of heart disease data in different organizations, on which an ML model will run classification algorithms to predict heart disease in the patient. Choose the Training Status tab and wait for the training run to complete. Choose New Application.

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

AWS Machine Learning Blog

The insurance provider receives payout claims from the beneficiary’s attorney for different insurance types, such as home, auto, and life insurance. When this is complete, the document can be routed to the appropriate department or downstream process. Custom classification is a two-step process.

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UC Berkeley Researchers Propose CRATE: A Novel White-Box Transformer for Efficient Data Compression and Sparsification in Deep Learning

Marktechpost

Such a representation makes many subsequent tasks, including those involving vision, classification, recognition and segmentation, and generation, easier. Therefore, encoders, decoders, and auto-encoders can all be implemented using a roughly identical crate design. Furthermore, the crate model exhibits many useful features.

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning Blog

Furthermore, the dynamic nature of a customer’s data can also result in a large variance of the processing time and resources required to optimally complete the feature engineering. Most of this process is the same for any binary classification except for the feature engineering step.

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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

You can deploy this solution with just a few clicks using Amazon SageMaker JumpStart , a fully managed platform that offers state-of-the-art foundation models for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval.

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