## 3 Case Study--Space Systems  
In this section, we present an industrial case study in the domain of space systems, which serves both to motivate the need for systematic reuse of machine learning components and to illustrate the practical application of the ML PLE framework. The case involves a company that has developed a family of space systems for multiple satellite missions. These systems are highly data-intensive and apply machine learning techniques to a variety of critical tasks, including telemetry analysis, anomaly detection, trajectory optimization, and payload data interpretation [22; 23; 24; 25]. Despite the widespread use of machine learning across these systems, we observed that the reuse of machine learning assets such as datasets, models, feature pipelines, and training workflows was minimal [24]. Most machine learning solutions were developed independently for each mission, leading to redundancy, inconsistencies, and additional development effort. This observation highlighted the need for a structured approach to reuse, and served as the motivation for applying the ML PLE framework. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **ML PLE** & **MLOps Platforms** \\ \hline Primary Objective & Systematic reuse and variability management across multiple ML products. Product family level. Asset management, product derivation, lifecycle traceability. Strategic and architectural. Includes domain engineering and application engineering processes. & Automation, monitoring, and deployment of individual ML models. Single model or pipeline level. Continuous integration and deployment (CI/CD). Operational implementation. Emphasizes model training, serving, and monitoring. \\ Variational Management Configuration & Central to the framework. Guided by feature models and asset readiness levels. Core asset modularization, asset readiness levels, Fault ML lifecycle including scoping, design, configuration, monitoring. & Suided by pipeline orchestration and code templates. Pipeline reuse, model registries, environment packaging. Primarily post-training: deployment, versioning, performance monitoring. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing ML PLE with MLOps Platforms. Space systems and machine learning systems share several important characteristics. Both involve complex modular architectures, exhibit variability across deployments, and operate within iterative and data-driven environments. They also require rigorous traceability, validation, and adaptability in response to changing operational demands. These commonalities provide a strong rationale for using the space systems domain to demonstrate the relevance and effectiveness of product line engineering principles in the context of machine learning. Figure 4 presents a conceptual model of a typical space system, composed of four main segments: Launch Segment, Ground Segment, Space Segment, and User Segment. These segments are common to nearly all space missions, including both commercial and scientific applications. They may be co-located or geographically distributed and are often operated by different organizations. Each segment functions as a complex system that integrates hardware, software, and data. The Launch Segment includes the launch vehicle and associated facilities for integration and testing prior to launch. The Space Segment comprises the satellite itself, including the satellite bus, payload management systems, command and data handling units, telemetry tracking and command modules, onboard software, and communication antennas. The Ground Segment is essential for mission control and data processing, integrating systems for mission planning, command and control, flight dynamics, and analytics. Mission planning ensures conflict-free and resource-efficient operations. Command and control monitors satellite status and transmits operational commands. Flight dynamics handles satellite stability, orbit determination, trajectory optimization, and collision avoidance.