Striking the Balance between Supervised and Unsupervised Machine Learning


Today, a fresh generation of technologies, fuelled by advances in artificial intelligence based on machine learning, is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors. To stay one step ahead of the pack, businesses not only need to understand machine learning complexities but be prepared to act on it and take advantage. After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behaviour by recognising complex data patterns and uncovering the precise signatures of degradation and failure. They can then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The leading software constructs are autonomous and self-learning.

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