Generative Data Intelligence

Tag: distributed training

KT’s journey to reduce training time for a vision transformers model using Amazon SageMaker | Amazon Web Services

KT Corporation is one of the largest telecommunications providers in South Korea, offering a wide range of services including fixed-line telephone, mobile communication, and...

Unpatched Critical Vulnerabilities Open AI Models to Takeover

Researchers have identified nearly a dozen critical vulnerabilities in the infrastructure used by AI models (plus three high- and two medium-severity bugs), which could...

Fine-tune Whisper models on Amazon SageMaker with LoRA | Amazon Web Services

Whisper is an Automatic Speech Recognition (ASR) model that has been trained using 680,000 hours of supervised data from the web, encompassing a range...

Putting AI challenges in perspective with partnerships

Sponsored Feature As the technology becomes more widely deployed across more vertical sectors and industries, the capacity of artificial intelligence (AI) to transform business...

From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker | Amazon Web Services

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Founded in 2011, Talent.com is one...

How Meesho built a generalized feed ranker using Amazon SageMaker inference | Amazon Web Services

This is a guest post co-written by Rama Badrinath, Divay Jindal and Utkarsh Agrawal at Meesho. Meesho is India’s fastest growing ecommerce company...

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium | Amazon Web Services

Large language models (LLMs) have captured the imagination and attention of developers, scientists, technologists, entrepreneurs, and executives across several industries. These models can be...

How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline | Amazon Web Services

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab, build an active learning framework on AWS...

Orchestrate Ray-based machine learning workflows using Amazon SageMaker | Amazon Web Services

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need...

Falcon 180B foundation model from TII is now available via Amazon SageMaker JumpStart | Amazon Web Services

Today, we are excited to announce that the Falcon 180B foundation model developed by Technology Innovation Institute (TII) and trained on Amazon SageMaker is...

Announcing the Preview of Amazon SageMaker Profiler: Track and visualize detailed hardware performance data for your model training workloads | Amazon Web Services

Today, we’re pleased to announce the preview of Amazon SageMaker Profiler, a capability of Amazon SageMaker that provides a detailed view into the AWS...

Machine learning with decentralized training data using federated learning on Amazon SageMaker | Amazon Web Services

Machine learning (ML) is revolutionizing solutions across industries and driving new forms of insights and intelligence from data. Many ML algorithms train over large...

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