## 2 Background and Related Work
### Related Work  
Reuse in AI and ML has been a relevant attention point aimed at reducing duplication of effort and enhancing scalability across projects and domains [6; 7; 8; 9]. While a full systematic literature review of machine learning reuse techniques is beyond the scope of this paper, we acknowledge its importance and intend to address it in a dedicated follow-up study. In this work, we provide a focused overview of representative reuse techniques to frame the relevance and motivation for the ML PLE framework. The number of key reuse approaches that were identified is described below.  
Reusable datasets form the basis of many ML systems, particularly for supervised learning tasks where data collection and labeling are expensive [10; 11]. Publicly available datasets provide a starting point for training and benchmarking models. Enhancements like data versioning, augmentation, and traceability enable datasets to be adapted for new tasks while maintaining reproducibility.  
Code reuse is widely facilitated using ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. These tools provide modular implementations of training routines, model architectures, evaluation metrics, and loss functions, enabling developers to build upon proven components and maintain uniform development practices [12].  
Pretrained models and transfer learning build on these foundational components, offering reusable, general-purpose models trained on large datasets, such as BERT, ResNet, and GPT [13; 14; 15; 16]. Transfer learning allows developers to adapt these pretrained models for new tasks by fine-tuning only specific layers, significantly reducing training time and computational costs. Pretrained models are especially powerful when combined with reusable datasets and feature pipelines, creating a comprehensive foundation for ML development.  
Another reuse approach in ML involves the development and deployment of feature engineering pipelines [17; 18; 19]. These modular workflows are designed to extract, transform,  
Figure 3: Traditional Product Line Engineering Lifecycle Process.  
and select features from raw data in a standardized manner. A well-designed preprocessing pipeline can often be reused across multiple datasets or tasks--for example, adapting a time-series transformation pipeline for various anomaly detection applications with only minor adjustments.  
Reusable hyperparameter tuning strategies address the optimization phase by defining configurable parameter ranges that can be applied across different models and tasks [20, 21]. Tools like Optuna and Ray Tune automate the search for optimal configurations, reducing duplication of effort and improving efficiency.  
Reusable evaluation metrics and benchmarks are essential for enabling systematic reuse in machine learning. Standard metrics such as accuracy, F1-score, and mean squared error can be applied consistently across different models and tasks, supporting uniform evaluation practices. Benchmarks like General Language Understanding Evaluation (GLUE) for NLP [15], and DAWNBench, a benchmark suite for end-to-end deep learning training and inference [22], provide shared protocols and datasets that facilitate reproducibility and comparability. Within an ML PLE context, these reusable evaluation components help ensure consistent quality across product variants, reduce redundancy in evaluation design, and enhance traceability throughout the lifecycle. As such, they are key elements in supporting scalable and reliable ML reuse.