Identify bottlenecks, improve resource utilization, and reduce ML training costs with the deep profiling feature in Amazon SageMaker Debugger

Favorite Machine learning (ML) has shown great promise across domains such as predictive analysis, speech processing, image recognition, recommendation systems, bioinformatics, and more. Training ML models is a time- and compute-intensive process, requiring multiple training runs with different hyperparameters before a model yields acceptable accuracy. CPU- and GPU-based distributed training

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Shared by AWS Machine Learning December 10, 2020

Making sense of your health data with Amazon HealthLake

Favorite We’re excited to announce Amazon HealthLake, a new HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud, at petabyte scale. HealthLake uses machine learning (ML) models trained to automatically understand and extract meaningful medical

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Shared by AWS Machine Learning December 10, 2020

How KM helped "the Great Crew Change" in the oil sector.

Favorite Knowledge Management has been a key factor in easing the demographic gap as older retiring oil-sector workers gave way to younger counterparts. Royalty-free image from Pixabay At the beginning of this century, the oil sector realised it was was facing a crisis of both manpower and knowledge.   During the

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Shared by Nick Milton December 10, 2020

Incremental learning: Optimizing search relevance at scale using machine learning

Favorite Amazon Kendra is releasing incremental learning to automatically improve search relevance and make sure you can continuously find the information you’re looking for, particularly when search patterns and document trends change over time. Data proliferation is real, and it’s growing. In fact, International Data Corporation (IDC) predicts that 80%

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Shared by AWS Machine Learning December 9, 2020