THE 32nd INTERNATIONAL
Pipeline Pigging & Integrity Management Conference™
plus Training Courses and Exhibition
George R. Brown Convention Center and the Marriott Marquis Hotel
|February 17-18||February 19-21||February 18-20|
|7:30am||Registration & coffee|
Attendees will learn how to apply inferential statistical and machine learning methods to common pipeline integrity and risk management use cases. In this interactive hands-on course, participants will be presented the technical basis of machine learning fundamentals and will use their own data with open source software to experience its practical application.
This is a practical hands-on course structured as a “teach and do,” where attendees will be presented the technical concepts of machine learning which they will then apply to their data. The objective of the course is for the attendee to learn how machine learning methods can support the following use cases:
On completion of the course, participants will be awarded 1.4 CEUs.
Michael Gloven, P.E. is Managing Partner of EIS (www.expertinfrasolutions.com) a Denver-based engineering consultancy focused on machine learning and risk management practices for the natural gas, hazardous liquids and water industries. He started his career with Conoco Pipeline holding various operational, engineering and management positions throughout the US. He then went on to co-found Bass-Trigon, a software and engineering company, and as president helped build the organization as a leading provider of risk and cathodic protection data management software. After acquisition of Bass-Trigon, he became an advisor to several international energy and technology companies, and soon afterwards started a new company focused on API 1173 process management for the pipeline industry which was then acquired in 2015. For the last several years, Mike has merged his software, engineering and domain expertise with machine learning practices to advance problem solving in the pipeline industry.
It will be necessary to bring a laptop to this course