May 30, 2019 - May 31, 2019
8:00 am - 12:00 pm
Mike Gloven will present “Application of Machine Learning to Pipeline Integrity – Case Study,” on May 30, 31, 2019 in Calgary, AB (CAN).
Students are required to bring their laptop to this training.
12 PDHs available with Certificate of Completion.
Learn the basics of Machine Learning thru use of participant or course provided data. This is a hands-on course to work with the fundamentals of performing Machine Learning, so the participant will have the know-how to apply these to their business.
- Machine Learning reveals useful patterns hidden within complex data streams for the purposes of understanding what is likely to happen in the future,
- Understanding these patterns supports identification of higher risk or threat susceptible assets subsequently enabling more efficient intervention and proactive mitigation,
- Machine Learning improves the efficiency of data management activities by predicting the most influential variables impacting integrity objectives,
- As a data driven algorithmic approach, Machine Learning may be used to improve or replace existing risk analysis applications
- Integrity Managers & Engineers
- Risk Managers & Engineers
- Risk Analysts
- GIS Analysts
- Data Scientists
This 1½ day class allows the participant to put into practice the basics of Machine Learning against their pipeline data or example data provided thru the course. Although the Machine Learning Basics course is not a prerequisite, it is helpful to have this knowledge to obtain the most value out of the Case Study course. This course is a hands-on class using MS Excel and an Open Source Machine Learning platform to gain insights from pipeline data. A “template” data structure will be provided to support data requirements if the participant brings their own data. The value of this class is allowing the participant to gain insights by working with data applicable to their integrity management requirements.
- Introduction & Course Objectives
- Overview of Machine Learning Basics
- Overview of Machine Learning Process
- Data Preparation
- Data Quality & Feature Analysis
- Method Evaluation & Selection
- Validation & Simulation
- Integration of Results wIntegrity Program
The attendee will:
- Learn the basics and process of using Machine Learning
- Learn how to prepare data (relational, spatial, dynseg, aggregation, missing data, etc.)
- Learn how to measure data quality & select the most influential features
- Learn how to select a learning model (classification, regression, ensemble, etc.)
- Learn how to measure model performance (training data, validation data, confusion matrix, etc.)
- Learn considerations for integrating Machine Learning into your integrity program