## 6 Discussion and Conclusions  
In this study, we propose a novel technological framework that takes the application of machine learning technologies in predictive and prescriptive maintenance to another level by integrating a self-managed, cloud-enabled monitoring platform. This framework is novel in the sense that it has the capability to automatically oversee the entire predictive model lifecycle through the integration of continuous performance monitoring, automatic retraining workflows, and seamless redeployment without human intervention. This level of automation and autonomy addresses central limitations in the current literature, wherein most contributions focus solely on maximizing predictive accuracy or propose maintenance approaches independently of the sustainability and long-term validity of the models implemented. Our solution also exceeds the state of the art by incorporating DevOps-oriented practices within industrial maintenance processes, enabling a structured model governance and lifecycle management strategy, thereby ensuring that predictive models remain accurate and valid over time despite operational variability and evolving degradation patterns. Such an approach is particularly valuable in industrial environments where concept drift and model accuracy degradation are significant concerns for the reliability of predictive maintenance systems. Importantly, the contribution of this work lies not in the use of a specific machine learning approach but in the development of a robust and flexible methodological framework that is independent of the underlying predictive models. Although long short-term memory (LSTM) networks and Reinforcement Learning (RL) were utilized due to the nature of the available dataset and the personalized nature of the physical variables being tracked, this system is designed to be capable of accommodating more advanced modeling techniques as they are made available. In particular, while Transformer-based models have shown a lot of promise in other domains, their application here is hindered by both the limited quantity of data and the highly technical nature of the predictive features. However, the framework described herein provides the architectural foundations required to include such models when appropriate datasets are created. The results concerning the learning curves show the importance of the quantity of data obtained in increasing the accuracy of this model, especially with respect to the initial data used during the learning process. These initial data allowed for a large reduction in the validation MSE value. However, the validation curve shows the importance of optimizing the regularization parameters for the ElasticNet model, showing the tendency to overfit when the model is not optimized. In this study, we explore the capabilities of different types of models to make predictions of behavior for industrial assets considering the sensitivity of the hyperparameters and the time window ahead for these predictions. According to the findings, and from the perspective of their industrial application, some models can estimate behaviors for the operation of assets regarding the interesting variables, not only regarding accuracy but also uncertainty levels when predictions are considered. In fact, different models can contribute differently to these goals. This paper also provides a reference framework for enabling the integration of such models into industrial practice. Traditionally, this approach requires model deployment and technical implementation steps [81]. However, in most cases, the proposals do not consider the general perspective of technical integration, monitoring, and updating. Instead, these concerns are addressed case by case. Due to the increase in the usage of IoT already discussed in the Industry 4.0 context, an accelerated convergence between Information Technology (IT) and Operational Technologies (OTs) has emerged [82; 83].