Remove Auto-classification Remove Auto-complete Remove Information
article thumbnail

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.

article thumbnail

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.

Metadata 119
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

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.

article thumbnail

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.

article thumbnail

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).

article thumbnail

Introduction to Graph Neural Networks

Heartbeat

They are as follows: Node-level tasks refer to tasks that concentrate on nodes, such as node classification, node regression, and node clustering. Edge-level tasks , on the other hand, entail edge classification and link prediction. Graph-level tasks involve graph classification, graph regression, and graph matching.

article thumbnail

Scaling Thomson Reuters’ language model research with Amazon SageMaker HyperPod

AWS Machine Learning Blog

Thomson Reuters , a global content and technology-driven company, has been using artificial intelligence and machine learning (AI/ML) in its professional information products for decades. They are professionals with discerning information needs in legal, corporate, tax, risk, fraud, compliance, and news domains. 55 440 0.1