## 2 Background and Related Work
### Limitations of Existing ML Reuse Approaches and the Motivation for ML PLE  
Despite existing and ongoing efforts, significant limitations remain in achieving systematic and large-scale reuse in machine learning. Existing approaches typically emphasize isolated components such as models, datasets, or individual pipelines, without integrating them into a cohesive framework that spans the complete machine learning lifecycle. This narrow focus and the fragmentation of current reuse strategies often lead to inefficiencies in managing interdependencies. Common issues include misalignment between preprocessing components and model expectations, as well as compatibility challenges between retrained models and deployment environments. In addition, feedback mechanisms are frequently implemented manually and are unstructured, making it difficult to maintain traceability and consistency across iterative development cycles.  
A prominent class of recent solutions to improve ML development and deployment is MLOps (Machine Learning Operations). MLOps platforms such as MLflow, Kubeflow, and TFX are designed to support the operational lifecycle of machine learning, offering automation, orchestration, and continuous integration and delivery (CI/CD) [6, 11, 17]. These tools help manage training, deployment, and monitoring of models in production settings and are effective at enabling reproducibility and traceability at the level of individual pipelines. However, MLOps does not address reuse at a strategic, system level across families of ML-enabled products. It primarily focuses on the operationalization of single ML solutions, and does not include concepts such as variability modeling, asset readiness classification, or coordinated management of reusable core assets like datasets, models, and pipelines. To clarify the distinction between these paradigms, Table 1 presents a comparative overview of MLOps and the proposed ML PLE approach.  
These limitations and the lack of a holistic and systematic reuse point to the need for a unified reuse framework that integrates traceability, supports structured variability management, and promotes automation across the machine learning lifecycle. In contrast to the current approaches, the proposed ML PLE framework introduces a comprehensive, system-level approach to reuse that is inspired by principles from PLE. ML PLE organizes machine learning development around reusable core assets, including datasets, models, pipelines, and hyperparameters. It provides explicit support for managing variability and configuring reusable components across a family of machine-learning-enabled products.  
The originality of ML PLE lies thus in essence in its holistic adaptation of PLE concepts to the domain of machine learning. This includes both technical elements such as asset modularization, reusable pipelines, and asset readiness classification, as well as a structured methodology consisting of dedicated domain engineering and application engineering phases tailored to machine learning workflows. In this way, ML PLE offers a strategic extension to conventional reuse practices in machine learning. It bridges the gap between high-level planning for product families and the concrete execution of machine learning workflows, an area that is not fully addressed by current lifecycle management platforms. We further elaborate on the theoretical positioning of ML PLE in Section 6.1, where we explicitly compare it with major ML reuse paradigms.  
Based on the above comparison, we can thus conclude that while MLOps is vital for operationalizing ML solutions, ML PLE complements it by focusing on managing variability, large-scale and systematic reuse, and traceability across multiple ML systems. MLOps focuses on the execution and automation of pipelines, whereas ML PLE governs the structured reuse of the underlying assets and configurations at scale.