It’s time to throw both IT and the AI Data Scientist a life preserver!

By Dan Kuczkowski | March 21, 2019

Everyone in the computer business knows just how hot the machine learning (ML) space is today.  The promise, as well as the demands, being placed on the AI data scientist by their companies, are numerous.  For most of these people, their GPU laden computers used to run analysis are viewed as just a tool. Often, each data scientist is provided with their own powerful computer and they don’t want to be burdened with the need to operationalize these computers. Simply put, they just want to run their jobs as quickly as possible.

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AI Operations: It Can’t Be Just an Afterthought

By Grant Gustafson | October 30, 2018

I’d like to draw your attention to a webinar taking place tomorrow, Wednesday October 31st, at 11am CDT. This is a must-attend webinar for anyone who is exploring how to deploy and manage an Artificial Intelligence (AI) environment. 

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Bright and Dell EMC Collaborate on Joint Development of Machine Learning Solutions

By Grant Gustafson | March 23, 2018

Hot on the heels of the Dell EMC HPC Community Meeting which took place earlier this week, we’re gearing up for NVIDIA GTC, March 26-29 in San Jose, and I’m pleased to say that Bright will be present on the Dell EMC booth at the event.

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Bright, Dell and NVIDIA Delve into Deep Learning

By Lee Carter | July 03, 2017

With Deep Learning experiencing unprecedented momentum in the HPC market, I find it exciting that Bright’s development team is focusing in on this area, and is involved in cutting edge Deep Learning innovation.

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Deep Learning in Bright Cluster Manager Version 7.3

By Panos Labropoulos | October 11, 2016

Enterprises have been busy for years collecting large amounts of data and analyzing it to obtain a competitive advantage by using machine learning – developing algorithms that can learn from and make predictions on data. Now some are looking to go even deeper – using machine learning techniques called deep learning to create predictive applications for fraud detection, demand forecasting, click prediction, and other data-intensive analyses.

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How to Easily Deploy and Manage Machine Learning Libraries and Tools

By Panos Lampropoulos | April 05, 2016

In recent years, the focus of big data analytics has shifted from simple statistical inference to sophisticated Machine Learning algorithms. Machine Learning (ML) can be understood as a set of analytical tools that collectively derive a model based on a set of observations.  Simple data modeling is now deemed insufficient because it is based on examining trends in data, but often ignores subtle features and can cause data analysts to miss the “big picture”.

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