Toggle Menu


NOVEMBER 17-18, 2021
A virtual event hosted via GoToWebinar


Practical Application of Machine Learning to Pipeline Integrity
NOVEMBER 15-16, 2021


Day 1
  7:30am Registration & coffee
  8:00am-5:00pm Course
Day 2
  8:00am-5:00pm Course


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.

Course Objectives

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:

  • Learn & validate data driven algorithms
  • Validate existing rule-based algorithms
  • Measure the influence and importance of underlying threat data
  • Infer missing or unknown data
  • Establish optimal assessment intervals
  • Support the assessment of un-piggable pipelines
  • Support monetized risk-based decision-making

Who should attend

  • Integrity managers & engineers
  • Risk managers & engineers
  • Data analysts


On completion of the course, participants will be awarded 1.4 CEUs.

Preparation to attend

  • Install machine learning desktop software on PC (a download link and installation instructions will be provided prior to the course)
  • Prepare example data set (template will be provided)
  • Watch software instructional videos (<2 hrs)


  • Presentation slides and related documentation will be printed in full color in spiral-bound book form as well as PDF download
  • Machine learning configuration file (example machine learning processes)


Michael Gloven, P.E. is Managing Partner of Pipeline-Risk ( 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

Technical content

  • Fundamental Elements of Machine Learning as a Strategic Process
  • Requirements for Data Preparation & Quality Assurance
  • Inferential Statistics, Hypothesis Testing, Confidence Intervals
  • Cognitive vs. Machine Learning Bias
  • Math Fundamentals (Role of Linear Algebra, Statistics and Calculus)
  • Feature Analysis (Information Gain, Correlation, Mutual Information, Deviation)
  • Feature Engineering (Principal Component Analysis)
  • Feature Selection (Forward and Back Propagation, Genetic Algorithms)
  • Outlier Detection Methods
  • Sampling Techniques, Cross-Validation
  • Regression (Linear, Generalized, GBT, SVM, Polynomial, Deep Learning)
  • Classification (CART, Bayes, KNN, Logistic Regression, GBT, Random Forest, DL)
  • Clustering (X-Means, K-Means)
  • Bias-Variance Trade-Off, Validation & Performance, Model Comparison
  • Confusion Matrices, ROC Curves, Learning Curves
  • Model Simulation and Sensitivity Analysis
  • Model Application to Support Decision-Making
  • Extending Methods to Support Monetized Risk Analysis
  • Overview of Machine Learning Technology Options (R, Python, TensorFlow)
  • Popular Learning Resources

 Organized by:

Clarion Technical Conferences