On the reasons for the failures of machine learning projects

Dmitry Namiot, Eugene Ilyushin

Abstract


This article analyzes the errors and causes of failure of projects using machine learning. Technically, according to academic articles, the percentage of failed projects is quite high. Machine learning systems naturally depend on data. Therefore, the simplest answer to the question about failures is an explanation related to data problems. But the problems with the success of projects are actually quite large - figures such as 87% of unsuccessful projects are given in the literature. Therefore, more detailed explanations are needed - in the face of such a large number of failures, the task of analyzing such errors becomes more than relevant. The article, based on many analyzed works, presents summary data on errors and failures of projects using machine learning, and analyzes the relationship of these causes with the requirements for the stability of designed systems. It is shown that most of the reasons are, in fact, the lack of stability for machine learning systems. The paper also shows the importance of the transition to data-centric systems, and presents forecasts for the further development of machine learning models for critical applications.

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References


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