Build a GNN-based real-time fraud detection solution using the Deep Graph Library without using external graph storage

Favorite Fraud detection is an important problem that has applications in financial services, social media, ecommerce, gaming, and other industries. This post presents an implementation of a fraud detection solution using the Relational Graph Convolutional Network (RGCN) model to predict the probability that a transaction is fraudulent through both the

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Shared by AWS Machine Learning March 1, 2023

Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

Favorite In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. It enables them to unlock the value of their data, identify trends, patterns, and predictions, and differentiate themselves from their competitors. For example, in the healthcare industry, ML-driven analytics can

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Shared by AWS Machine Learning March 1, 2023

Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

Favorite In recent years, advances in computer vision have enabled researchers, first responders, and governments to tackle the challenging problem of processing global satellite imagery to understand our planet and our impact on it. AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art

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Shared by AWS Machine Learning February 25, 2023

AWS and Hugging Face collaborate to make generative AI more accessible and cost efficient

Favorite We’re thrilled to announce an expanded collaboration between AWS and Hugging Face to accelerate the training, fine-tuning, and deployment of large language and vision models used to create generative AI applications. Generative AI applications can perform a variety of tasks, including text summarization, answering questions, code generation, image creation,

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Shared by AWS Machine Learning February 22, 2023

MLOps deployment best practices for real-time inference model serving endpoints with Amazon SageMaker

Favorite After you build, train, and evaluate your machine learning (ML) model to ensure it’s solving the intended business problem proposed, you want to deploy that model to enable decision-making in business operations. Models that support business-critical functions are deployed to a production environment where a model release strategy is

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Shared by AWS Machine Learning February 22, 2023