4Atmos Technologies Announces Strategic Partnership with Mtell

By | January 15th, 2016|News, Partners, Predictive Maintenance|

The Partnership Provides Transportation and Industrial Markets with Implementation and Consulting Services for Mtell Software

WAXAHACHIE, TEXAS (January 14, 2016) – 4Atmos Technologies LLC, a technical consulting firm for transportation and industrial markets, today announced it has entered into a strategic partnership with Mtell, a leading asset health software solution provider […]

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Mtell Partners with OSIsoft to Enable Asset Prescriptive Maintenance

By | October 30th, 2015|Mtell Previse, News, Oil and Gas, Partners|

New partnership integrates Mtell’s Machine Learning Technology with the OSIsoft’s PI System to Optimize Asset Health for Industrial Enterprises

SAN DIEGO (OCTOBER 29, 2015) – Mtell, a leading asset health software solution company and provider of machine learning technology for the Industrial Internet of Things (IIoT), today announced it has partnered […]

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Fixing Problems Before They Arise in Oil and Gas

By | August 1st, 2015|Big Data, Conference, Industrial Internet of Things, Machine Learning, Mtell Reservoir, Oil and Gas, Partners|

Originally posted by Arunima Kumar in SAP Business Trends on Jul 20, 2015

As part of its IoT kickstarter program, the SAP Co-innovation Lab in Silicon Valley partnered with Mtell and Rolta on a solution for predictive machine maintenance in oil and gas. Built on SAP HANA, the solution […]

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Mtell and MapR Deploy Big Data Platform for Oil and Gas to Manage Real-time and Historical Sensor Data

By | March 26th, 2015|Big Data, Machine Learning, Mtell Previse, Partners, Predictive Analytics, Predictive Maintenance, Press Release|

Collaborative Hadoop-based solution handles over 100 million data points per second to enable predictive maintenance on oil rigs

SAN DIEGO and San Jose, Calif. – March 25, 2015 – Mtelligence Corporation (dba Mtell), and MapR Technologies, Inc., provider of the top-ranked distribution for Apache™ Hadoop®, today announced […]

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