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

Flipboard

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

AWS Machine Learning Blog

Structured data, defined as data following a fixed pattern such as information stored in columns within databases, and unstructured data, which lacks a specific form or pattern like text, images, or social media posts, both continue to grow as they are produced and consumed by various organizations.

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

AWS Machine Learning Blog

However, the sharing of raw, non-sanitized sensitive information across different locations poses significant security and privacy risks, especially in regulated industries such as healthcare. Insecure networks lacking access control and encryption can still expose sensitive information to attackers.

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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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FastAPI Meets OpenAI CLIP: Build and Deploy with Docker

Flipboard

Interactive Documentation: We showcased the power of FastAPIs auto-generated Swagger UI and ReDoc for exploring and testing APIs. This shared embedding space enables CLIP to perform tasks like zero-shot classification and cross-modal retrieval without additional fine-tuning. We Made It!

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

Becoming Human

Each node is a structure that contains information such as a person's id, name, gender, location, and other attributes. The information about the connections in a graph is usually represented by adjacency matrices (or sometimes adjacency lists). A typical application of GNN is node classification. their neighbors’ labels).

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Optimize pet profiles for Purina’s Petfinder application using Amazon Rekognition Custom Labels and AWS Step Functions

AWS Machine Learning Blog

This post details how Purina used Amazon Rekognition Custom Labels , AWS Step Functions , and other AWS Services to create an ML model that detects the pet breed from an uploaded image and then uses the prediction to auto-populate the pet attributes. Start the model version when training is complete.