# Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions
## 1 Introduction  
With the fourth industrial revolution, also referred to as Industry 4.0, a new wave of automation set off carrying novel industrial applications that promise to redefine the interaction between man and machine [1]. Businesses are increasingly shifting their service models from traditional automation, in which the integration of software systems on service platforms mainly depends on the outstanding programming skills of human software developers and the deployment of appropriate application programming interfaces (APIs) [2; 3], to engineered systems that integrate sensing abilities, computational prowess, control, and network utilities into _cyber_-physical objects that can connect to each other over the 'Internet of Things' (IoT) [4]. Data are a conspicuous hallmark of Industry 4.0 technologies [5]. Examples range from cloud computing [6], where large volumes of data can be stored, analysed, and processed more efficiently and cost-effectively with the cloud, over edge computing [7], which provides low end-to-end latency in real-time production operations [8] by allowing efficient near-sensor data analysis [9], to digital twins [10], where simulations of multiple systems' processes can be run on virtual environments, pulling data from IoT sensors and whatever objects connected to the Internet [11]. Of particular importance in moving data around the grid are the fields of artificial intelligence (AI) and machine learning (ML) [12]. Together, they can facilitate the creation of sustainable solutions and scalable business models by taking full advantage of available data volumes in a way that exceeds the use of the data generated within the concerned business field [13; 14] to include the data obtained from other fields and businesses [15]. At its core, an AI system is mainly an algorithm (code) that solves a problem by learning prototypical features (patterns) from large volumes of data. Data could be in any of different forms (for example, text, audio, image, and/or video). ML is a subfield of AI that characterises the system's capacity to spot patterns that are otherwise undetectable by humans [16]. It achieves this by using general-purpose procedures, which enable the AI system to solve the problem at hand without being explicitly programmed to do so [17]. Yet, for this to occur, the system needs to process data in a way that facilitates interpretation and provides context [16]. Typically, this is made possible by letting human experts label the data as part of the data preparation process, which includes collecting, curating, cleaning, and transforming raw data before processing and analysing it. This subtask is known as 'data annotation'. Currently, there are two different views within the ML community on how to improve the performance of AI systems: model-centric AI and data-centric AI. In model-centric AI, developers of an AI system successively upgrade a devised model (algorithm/code), while holding the volume and type of data collected fixed. Conversely, one can hold the model fixed and continuously improve the quality of data until reaching a high level of overall performance in terms of dealing with noise in the data. This is the data-centric AI approach (Figure 1). Due to the fact that the training processes in both approaches run in a continuous, iterative fashion, some refer to them as'model-cycle' and 'data-cycle' approaches, respectively, [18]. Over the past three decades, model-centric AI was dominantly used in both research and industry. In research, for example, more than 90% of the published AI research projects utilised a model-centric approach [19]. Recently, however, voices are getting louder in the promotion of data-centric AI. Reasons for this change of heart range from the lack of sufficiently large datasets [20] to the need for highly customised solutions [21]. The concept was introduced by [PERSON], founder of 'DeepLearning.AI' (an educational platform) and Adjunct Professor at Stanford University.