Practical Applications of Online Machine Learning

This chapter addresses prerequisites, challenges, and potentials of applying Online Machine Learning (OML) methods in practice. These aspects are illustrated by means of domain-specific examples from different application fields. One of these surveyed application fields is official statistics (Sect. 7.1). Section 7.1.1 shows, that OML offers forward-looking potential for official statistics, but presently also comes with a lot of challenges. Especially compliance with quality assurance procedures (Sect. 7.1.2) and integration into existing process architectures (Sect. 7.1.3) prove to be major challenges. A survey about Machine Learning (ML) usage in German and other international statistical institutions shows that OML is currently still rather a niche topic in official statistics (Sect. 7.1.4). However, there are also domains, closely linked to official statistics, that either already feature OML applications or show promising potential for OML usage (Sect. 7.1.5). The second surveyed application field is the process of hot rolling in the steel industry (Sect. 7.2). In general, the process quality of hot rolling (Sect. 7.2.1) benefits from ML predictions (Sect. 7.2.2). However, because of being susceptible to drift, the complex hot rolling process cannot be adequately described without models that are continuously updated (Sect. 7.2.3). These characteristics make industrial hot rolling a suitable use case for the application of OML (Sect. 7.2.4). General aspects important for using OML in practice are briefly summarized in Sect. 7.3. These include reflections about model deployment (Sect. 7.3.1) and considerations regarding differences in required labor hours in comparison to Batch Machine Learning (BML) (Sect. 7.3.2).