## 7 Conclusions  
Reuse in machine learning (ML) is critical for improving efficiency and scalability, yet current reuse practices remain fragmented and ad hoc. To address this, we have proposed Machine Learning Product Line Engineering (ML PLE), a systematic reuse framework that extends traditional Product Line Engineering (PLE) principles to ML-enabled systems. The framework introduces a structured approach to managing core ML assets, such as datasets, models, pipelines, and hyperparameters, while addressing variability and scalability challenges. Unlike existing reuse strategies that focus on isolated ML components, ML PLE introduces a structured reuse methodology at the system family level, enabling traceability, configurability, and reuse across the full ML lifecycle. By integrating domain engineering and application engineering processes, ML PLE facilitates systematic reuse and the efficient derivation of ML solutions tailored to diverse application needs. The novelty of this study lies in two complementary perspectives. From the PLE perspective, this is the first approach to systematically integrate ML-specific core assets into a product line engineering framework. Prior PLE efforts have largely excluded the data-driven and iterative nature of ML development. From the ML perspective, ML PLE introduces a methodology for large-scale, systematic reuse across ML product families, going beyond the current landscape of isolated reuse techniques such as transfer learning or fine-tuning. To validate the framework, we applied it to a real industrial case study in the space systems domain. As such, the insights are grounded in real-world practice. While the standard ROI equation has been applied and used qualitatively to compare development strategies, our future work will include a quantitative evaluation. This will involve instantiating the model with empirical data across benchmark tasks to assess reuse effort, variability management, and return on investment under ML PLE versus baseline approaches. A broader empirical validation would be beneficial to confirm generalizability. Moreover, certain aspects such as asset variability modeling and integration with MLOps pipelines require further elaboration and refinement as the framework evolves. Several important future research directions emerge from the findings of this study. A key priority is to advance the ML PLE framework to support emerging machine learning paradigms such as federated learning, edge deployment, and real-time adaptive systems. We also aim to further explore the automated management of ML asset variability, assess the scalability of reuse strategies in high-dimensional and multi-modal pipelines, and strengthen the alignment of ML PLE with the principles of responsible AI, particularly transparency, traceability, and compliance with evolving data governance standards. In addition to conventional PLE metrics, we will develop quantitative measures specifically tailored to the ML PLE context, focusing on reuse effectiveness, configuration effort, and lifecycle traceability across ML core assets. These metrics are intended to support rigorous empirical validation and enable comparative benchmarking with alternative reuse strategies. As part of our future work, we plan to conduct controlled evaluations using standardized benchmarks and task suites. Key evaluation indicators will include reuse effort, time-to-deployment, asset utilization coverage, and configuration complexity. These studies will be conducted across diverse domains, such as precision agriculture, industrial automation, and smart manufacturing, to systematically assess the scalability and generalizability of the ML PLE framework. Furthermore, we intend to extend the integration of supporting tools, with a focus on improving interoperability between feature modeling environments, configuration management systems, CI/CD pipelines, and MLOps platforms. This will facilitate the seamless instantiation, monitoring, and evolution of reusable ML artifacts at scale.