Machine learning (ML) is present in many aspects of our lives, to the point that is difficult to get through a day without having contact with it. The most common example is doing a simple Google search, trained to show you the most relevant results. But ML can also be found in our smartphones, through assistants like Siri or Alexa. The technology is also starting to approach safety critical domains as autonomous driving and surveillance powered by facial recognition.
All these applications have been made possible by a combination of research, commercial factors, and the availability of data for generating and training the models underlying them. In the industrial context there is also the promise that machine learning will help predicting when to perform maintenance on machinery, identify anomalies in machine operations, or help process engineers to identify the factors which make the difference between a good or bad product batch.
Although these introductory remarks by no means constitute a deep analysis of the relatively slow take-up of machine learning techniques in the industrial domain as compared with other areas, there are several factors which make its application in industries fundamentally more difficult than in products directed to the final consumer.
Click here to read the entire article, written by our data Scientist Fredrik Wartenberg.