Using GPUs for training Tensorflow models

In recent years, there has been significant progress in the field of machine learning. Much of this progress can be attributed to the increasing usage of graphics processing units (GPUs) to accelerate the training of machine learning models. In particular, the extra computational power has lead to the popularization of Deep Learning – the use of complex, multi-level neural networks to create models, capable of feature detection from large amounts of unlabeled training data.

GPUs are so well-suited to deep learning because the type of

Strata+Hadoop World NYC 2015 Reflections

Machine learning, cloud, visualization, Hadoop, spark, data science, scalability, analytics, terabytes, petabytes, faster, bigger, more secure, simply better. The kind of a merry-go-round that keeps spinning in your head after you spend three days on the exhibit floor at Strata+Hadoop conference. And lots of elephants, of course.

Not only did we attend Strata with fellow colleagues from DataArt and DeviceHive, we also helped our friends at Canonical and brought our demo to their booth. Canonical was showing Juju: a cloud infrastructure

DeviceHive at Strata+Hadoop World in NYC

DataArt will be showcasing Big Data, IoT and predictive maintenance solutions at Strata+Hadoop World NYC 2015, September 30 — October 1. Powered by Canonical’s Ubuntu Snappy Core and orchestrated by Juju, we will showcase how to deploy DeviceHive’s lambda architecture and evolve your industrial IoT solution from proof of concept to a scalable production system.

Stop by Canonical/Ubuntu Booth #358. If you would like to connect at the show, please leave your contact information: http://campaigns.devicehive.com/iot-dataart-canonical-strata-hadoop-world-nyc-2015.

Looking forward to seeing you at Strata+Hadoop World