## 7 Conclusions and Future Trends  
This study examined the effectiveness of machine learning models and MLOps integration for wind energy forecasting. A detailed comparative analysis of various machine learning models was conducted to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. An end-to-end MLOps pipeline was developed, leveraging SCADA data from a wind turbine in Turkiye. The research compared models using the RMSE metric for feature selection and optimization and explored in detail the impact of integrating machine learning with MLOps on the precision of energy production forecasts. Hyperparameter adjustments of models such as LightGBM increased model performance, and the difficulties encountered in the use of Docker and ML models were emphasized. While the integration of MLOps has accelerated development and deployment processes, technical and operational challenges were highlighted. Model performance was assessed using the RMSE metric, conducting a comparative evaluation across different models. The findings revealed that the RMSE values among the regression models ranged from 460 to 192. Focusing on enhancing LightGBM, the research decreased the RMSE value to 190.34. Despite facing technical and operational hurdles, the implementation of MLOps has proven to enhance the speed (latency of 9 ms), reliability (through Docker encapsulation), and scalability (using Docker swarm) of machine learning endeavors. Future studies can focus on researching new ML models, improving MLOps processes, and integrating real-time data analysis, thus improving the accuracy of wind energy forecasts and the operational efficiency of enterprises. Applying machine learning algorithms and automating the model lifecycle with MLOps can increase the accuracy and reliability of energy production forecasts, contributing to more effective resource management and decision-making processes of energy companies and grid operators. This approach accelerates data-driven decisions and improves operational efficiency, enabling a proactive response to fluctuations in renewable energy and better planning of energy supply. The unique point of this study is to accelerate the integration, development, and distribution processes of ML studies carried out on wind energy and wind energy potential in Turkiye with MLOps. Thus, a study that will be a reference for future studies will be created and wind energy forecast accuracy will be increased efficiently. Future studies can focus on examining ML models to increase the accuracy of wind energy forecasts for different geographical regions by covering larger and more diverse datasets. A more comprehensive application of deep learning techniques and time series analysis can significantly improve forecasting performance. Additionally, work on automation and optimization of MLOps processes can further accelerate model development and deployment processes and increase efficiency. Integration of real-time data streams can instantly update wind energy forecasts and provide flexibility in energy production planning. These approaches will support operational efficiency and sustainability in the wind energy sector, allowing for more accurate and reliable forecasts in energy markets. Conceptualization, S.O. and A.A.; methodology, S.O. ; software, S.O. ; validation, A.A. and S.O. ; formal analysis, A.A.; investigation, S.O. ; resources, A.A. and A.A.; data curation, A.A.; writing--original draft preparation, S.O. ; writing--review and editing, A.A. and S.O. ; visualization, S.O. and A.A.; supervision, A.A.; project administration, S.O. ; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. This paper is supported by the European Union's Horizon Europe research and innovation program under grant agreement No. 101084323, project BLOW (Black Sea Floating Offshore Wind). Not applicable. Internet: Wind Turbine Scada Dataset.