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Frugality meets Accuracy: Cost-efficient training of GPT NeoX and Pythia models with AWS Trainium

AWS Machine Learning Blog

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. Training steps To run the training, we use SLURM managed multi-node Amazon Elastic Compute Cloud ( Amazon EC2 ) Trn1 cluster, with each node containing a trn1.32xl instance.

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Amazon SageMaker built-in LightGBM now offers distributed training using Dask

AWS Machine Learning Blog

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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Bundesliga Match Facts Shot Speed – Who fires the hardest shots in the Bundesliga?

AWS Machine Learning Blog

His 2009 strike against Leverkusen at a speed of 125 km/h is one that is vividly remembered because the sheer velocity of Hitzlsperger’s free-kick was enough to leave Germany’s number one goalkeeper, René Adler, seemingly petrified. Simultaneously, the shot speed data finds its way to a designated topic within our MSK cluster.

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few steps. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few steps. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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Analyzing the history of Tableau innovation

Tableau

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Gestalt properties including clusters are salient on scatters. May 2017), which was Tableau’s first exploration of Machine Learning (ML) technology to provide computer assistance. Let’s take a look at each. .

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Analyzing the history of Tableau innovation

Tableau

Clustered under visual encoding , we have topics of self-service analysis , authoring , and computer assistance. Gestalt properties including clusters are salient on scatters. May 2017), which was Tableau’s first exploration of Machine Learning (ML) technology to provide computer assistance. Let’s take a look at each. .

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