E5.5 Final prototype for data aggregation for self-optimization processes on network operations

In the rapidly evolving landscape of network operations, the ability to self-optimize has become essential for maintaining optimal performance and adapting to dynamic conditions. Data aggregation is at the heart of these self-optimization processes, serving as the foundation for intelligent decision-making. Our final prototype uses this approach by deploying an ML model using Kubeflow Pipelines, which acts as the intelligent core of the cloud-based solution.The ML model uses data from a Feature Store for online inference, ensuring access to the most relevant and up-to-date information. This continuous stream of aggregated data allows the model to make accurate predictions and adjustments in real-time, enhancing the network's ability to self-optimize. By exporting the model's output to a Holoportation application, we demonstrate a practical application of our system, where intelligent data processing meets immersive user experiences. This integration highlights the seamless flow of data from aggregation to actionable insights, ultimately improving network performance and user satisfaction.The final prototype highlights the critical importance of data aggregation in facilitating self-optimization within cloud environments. By using Kubeflow Pipelines interconnected with a Feature Store, we have established a scalable and efficient pipeline for data processing and ML deployment. This framework not only improves the operational efficiency of network systems but also presents the transformative potential of combining machine learning with innovative technologies like Holoportation. Through this document, we illustrate how intelligent data aggregation is indispensable for advancing modern network operations.