## 5 ML PLE Framework
### Metamodel  
The metamodel that illustrates the concepts for this mapping is displayed in Figure 5. The adopted notation is based on UML. A product line is defined as a set of products developed using a two-phase lifecycle process: domain engineering and application engineering. The domain engineering process results in a core asset base that consists of different core assets. A core asset is a reusable artifact or component systematically developed to serve as the foundation for creating a family of related products. The application engineering process focuses on the development of a single product and reuses the core asset base. In addition, unanticipated assets that do not reside in the core asset base--the delta assets--can be used. When examining the intersection of ML and PLE, system development can be categorized into four distinct approaches, as illustrated in Figure 6. The first category, Single Non-ML, refers to the development of individual systems that do not incorporate ML and are not designed with reuse or product family considerations. The second category, Single ML, involves the development of individual systems that include ML components, but similarly lack a reuse-oriented or product line perspective. The third category, Non-ML PLE, represents traditional PLE approaches applied to conventional software systems without ML. The fourth and final focus of this study, ML PLE, involves the development of ML-enabled systems within a product family, guided by principles of systematic reuse, variability management, and coordinated lifecycle engineering. This last category forms the foundation of the ML PLE framework proposed in this paper. Figure 5: Metamodel for ML PLE Core Assets. Figure 6: ML System Development Strategies in PLE Context. When we consider ML PLE, new concepts and definitions need to be introduced:  
_Machine Learning Product Line (ML PL)_ is defined as a family of related ML-enabled products that share common core assets, such as datasets, models, feature pipelines, and training workflows, while differing in specific configurations or functionalities to meet diverse application requirements. ML PL focuses on systematic reuse and variability management to facilitate efficient development, deployment, and maintenance across various domains. _Machine Learning Product Line Engineering (ML PLE)_ is defined as a systematic approach to developing and managing a family of ML-enabled systems by applying PLE principles. It emphasizes maximizing reuse, managing variability, and ensuring scalability by treating datasets, models, pipelines, and hyperparameters as configurable and reusable core assets. _Machine Learning Core Asset (ML Core Asset)_ is defined as a reusable and configurable component that can be used as a building block for developing ML-enabled systems within a product line. Core assets in traditional software engineering--such as requirements, architecture models, and test artifacts--often trace to and depend on ML core assets like datasets, models, and pipelines when ML components are integrated into a system. Conversely, ML core assets also depend on SE assets for structural guidance, functional constraints, and validation strategies. This bidirectional relationship ensures alignment and traceability across the full system lifecycle. In principle, as derived from the above matrix in Figure 6, a product line focused exclusively on ML could be developed, emphasizing the large-scale, systematic reuse of ML capabilities within a defined product line scope. Alternatively, ML could serve as a component within a broader, conventional product line that focuses on the development of complete systems or products. In such cases, we can distinguish between two categories of core assets: General Core Assets and ML Core Assets. General Core Assets represent the traditional engineering lifecycle assets typically associated with PLE. These include requirements models, feature models, design models, code, and test artifacts. ML Core Assets, on the other hand, are specific to the ML domain.