## 1 Introduction  
Machine Learning (ML) has become a cornerstone of modern technology, finding applications across diverse domains such as predictive analytics, anomaly detection, and decision-making [1, 2]. Many ML applications address similar underlying tasks, presenting significant potential for systematically reusing core assets. Despite this, existing reuse practices in ML are often fragmented, limited in scope, and conducted on an ad hoc basis. Efforts typically focus on isolated components like pretrained models, datasets, or pipelines, without providing a cohesive framework that supports large-scale reuse across systems and projects. This lack of systematic reuse leads to inefficiencies, duplicated efforts, and challenges in scaling ML development for complex applications.  
In traditional engineering, Product Line Engineering (PLE) offers a well-established methodology for achieving large-scale systematic reuse [3, 4, 5]. By managing shared core assets--such as requirements, models, and code--PLE enables the efficient development of product families, reducing redundancy and improving consistency. However, conventionalPLE processes are not designed to address the unique characteristics of ML systems. Unlike traditional systems, ML systems rely heavily on data as a core asset, involve iterative workflows, and exhibit variability in datasets, models, and pipelines. These differences necessitate the adaptation of PLE principles to accommodate the specific needs of ML systems.  
To address this gap, we propose Machine Learning Product Line Engineering (ML PLE), a novel framework that extends PLE principles to ML systems. ML PLE systematically manages ML-specific core assets, such as datasets, feature pipelines, models, and hyperparameters, while aligning with the dynamic, data-driven nature of ML workflows. The proposed framework introduces key requirements for adapting PLE to ML and outlines a lifecycle process tailored to machine-learning-intensive systems. This approach enables systematic reuse, efficient variability management, and enhanced scalability for ML development.  
To validate the applicability and effectiveness of ML PLE, we applied the framework to an industrial case study in the context of space systems. The case study focuses on data analytics for satellite missions, demonstrating how ML PLE can address challenges such as managing variability in telemetry data, evolving datasets, and adapting workflows to mission-specific requirements. The results highlight the potential of ML PLE to improve reuse, scalability, and efficiency in ML-enabled space systems.  
The remainder of this paper is organized as follows. Section 2 provides background and reviews related work, including existing reuse practices in machine learning and their limitations. Section 3 describes the motivating space systems case study and illustrates the need for systematic ML reuse. Section 4 describes the requirements for adopting PLE for ML. Section 5 introduces the ML PLE framework, outlining its key concepts, ML Domain Engineering process, ML Application Engineering, and the integrated ML PLE process. Section 6 presents a qualitative evaluation based on the case study. Finally, Section 7 concludes the paper.