## 1 Introduction  
The motivations for industrial data processing have been the same for decades, namely to increase the revenue of investment by improving efficiency (i.e., by increasing productivity, and decreasing scrap, waste and energy usage), extending system lifetime, as well as enhancing safety and security. Sustainability has become yet another focal point of modern industry. As people acknowledged the necessity of distributed data collection and massive data processing in various industrial areas, the research and innovation domain of IIoT (Industrial Internet of Things) began to thrive. Its business drive was promoted by the Industry 4.0 initiative, whereas its applications were extended from the very much overlapping Cyber-Physical Systems (CPS) domain. There is no generic, de-facto architecture for IIoT systems, although a layered approach is followed by domain experts. The purpose of splitting the layers could vary from communication types due to infrastructure need to the ecosystem stakeholder point of view; hence three, four or five layers can be identified. Figure 1 provides a layered architectural view which shows the strong separation of technologies between the layers. It also indicates the different security approaches at the different layers [1]. While machine learning is exploited in various IIoT application areas, it is used only on a small subset of target areas extensively (see Figure 2). Depending on the application area, there are various purposes for processing industrial data. These include decision support, optimization, prediction, anomaly detection, classification, and clustering, just to name a few. In order to achieve the desired results, we need data--which are generally available for industrial players if IIoT-based data collection is in place--and we need physical resources for data processing--which are now available mostly due to the boom in GPU production. Because data and resources have been made available, we are able to use ML (Machine Learning) methods to achieve better results than ever before in the above areas. Figure 1: The architectural layers of IIoT systems [1]. Figure 2: Production stages typically covered by the process of manufacturing. While machine learning can be applied in all areas of manufacturing, only a small subset of them use machine learning techniques extensively (shown in bold typeset). In terms of finding details on these methods, the first resources to turn to are, naturally, textbooks. There are several great books on machine learning in general [2; 3; 4], and on modern tools regarding their application [5; 6; 7; 8]. Further, we can find survey papers on utilizing machine learning in the industry. The authors of [9] provide a survey of the upcoming wave of machine learning in smart manufacturing. The specific topic of tackling faults by machine learning (ML) in the industry 4.0 era are surveyed in [10]. In a paper on machine learning multi-agent systems [11], the authors focus exclusively on their application in the oil and gas industry. Regarding different levels of industry 4.0, [12] focuses on ML methods applied in production planning and control. Similarly, a review of ML methods for the optimization of production processes is provided in [13]. To provide comparison with the topic of our current article, we can find more specific papers summarizing ML methods for smart production in general [14], or that review ML for production energy efficiency [15]. The authors of [16] provide a comprehensive overview of prognostic methods in the area of Industry 4.0. The authors of [17] focus on sustainability and predictive maintenance. Regarding reliability engineering and safety, the authors of [18] provide a targeted survey. Furthermore, ML support on safety assurance is surveyed in [19]. The main contribution of this paper is that it provides a structured state-of-the-art view of the domain, with well-structured comparison tables and details of the knowledge in this industry thus far.