## 4 Discussion  
In this study, we revisited the CAAI developed in the KOARCH project. During the revision of this architecture, we briefly noted the existing limitations to automating the algorithm pipeline development process for all environments and use cases. This was mainly due to rapidly changing software interfaces and transfer complexities. While this highlighted the need for some human intervention, our focus quickly shifted to exploring the potential of OML in the context of production processes. Our attention centered on the notable absence of OML technology in the current CAAI framework, especially given the continuous data streams in CPPS. To illustrate the benefits of OML in industrial setups,  
\begin{table}
\begin{tabular}{l r r r} \hline \hline
**Parameter** & **Default** & **Tuned** & **Importance** \\ \hline grace\_period & 200 & 937 & 0 \\ max\_depth & 20 & 4 & 0.00 \\ delta & \(1\times 10^{-7}\) & \(1\times 10^{-6}\) & 0.00 \\ tau & 0.05 & 0.1 & 0.00 \\ leaf\_prediction & mean & adaptive & 88.66 \\ leaf\_model & linear regression & linear regression & 100.00 \\ model\_selector\_decay & 0.95 & 0.9 & 0.00 \\ splitter & EBSTS-Splitter & EBSTS-Splitter & 0.17 \\ min\_samples\_split & 5 & 2 & 0.00 \\ binary\_split & 0 & 1 & 0.00 \\ max\_size & 500 & 854.73 & 0.00 \\ memory\_estimate\_period & \(1\times 10^{6}\) & \(1.3\times 10^{6}\) & 0.00 \\ stop\_mem\_management & 0 & 0 & 0.00 \\ remove\_poor\_attrs & 0 & 0 & 0.00 \\ merit\_preprune & 0 & 0 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the default and tuned hyperparameters of the HTR for improved calculation time and memory consumption. The column on the far right illustrates the determined importance of the individual parameters during the tuning process.  
Figure 9: Evaluation results of tuned and untuned HTR models for MAE, computation time, and memory consumption. The X-axes are related to the evaluation steps. The tuning was performed with a focus on reducing time and memory consumption.  
we conducted experiments using a slitting machine setup. During testing, we compared the OML approach to three BML-based methods. The OML approach significantly outperformed the BML strategies, primarily due to its ability to update models iteratively, allowing for a swift response to concept drift in data. This feature is especially useful in machinery and predictive maintenance applications, presenting a more efficient alternative. Furthermore, the OML approach demonstrated computational benefits, showing not only time efficiency but also conservative memory usage. The performance was notably better than that of the other two methods, where the models were retrained following the initial training--namely, the shifting window and landmark strategies. In order to optimize the OML algorithm's performance, we introduced SMBO, to identify the best hyperparameter sets. Using SPOT for hyperparameter tuning significantly improved algorithm performance. Additionally, adjusting the priorities during the optimization phase successfully optimized computation time and memory consumption. This study underscores the potential of integrating OML technology and the effectiveness of hyperparameter tuning in enhancing CPPS. This research paves the way for the development of more agile and efficient Cyber-Physical Production Systems, ready to tackle the dynamic demands of industrial operations with refined precision and adaptability.