The term ‘Dynamic Heterogeneous Computing Sphere’ is used to describe a computing paradigm that is heterogeneous, volatile and highly dynamic.

The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.