Machine Learning

Example Decision Tree Model

Machine learning is a sub-field of artificial intelligence that includes most of the methods have led to tremendous practical breakthroughs in recent years. Self-driving cars, email spam filtering and online search all rely on machine learning algorithms to achieve their current level of performance. Netflix estimates that it saved $1 billion in 2016 as a result of their machine learning algorithm which increases customer retention by making personalized recommendations.

Machine learning algorithms do not require pre-defined relationships between variables but instead adjust to the patterns in your data in order to make a useful prediction. This highly flexible approach can be applied to wide array of problems, but users of these methods must but be careful to ensure that the model does not inadvertently pick up a biased view of the world from the data. The use of machine learning models in the field of engineering requires domain expertise to ensure that a model makes accurate and suitable predictions in it’s intended application with appropriate levels of conservatism.


Integral Engineering is pioneering the application of machine learning models to the energy industry by combining our extensive experience with statistical models, energy engineering and software development. Machine learning models can be used in applications such as optimizing predictive maintenance, automating outlier detection and assessing the reliability of existing assets. Integral Engineering is an Alberta Machine Intelligence Institute (AMII) innovation affiliate and works with their expert machine learning scientists to enhance our abilities to rapidly develop machine learning models to suit our client’s applications.

To find out how more about how your company can utilize machine learning, contact us today.

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