## 5 ML PLE Framework
### ML Domain Engineering Process  
The domain engineering process in ML PLE focuses on defining, developing, and managing reusable ML core assets while systematically addressing variability. This phase ensures these assets can effectively create diverse ML product variants during application engineering. Below is a description of the key stages in the ML PLE domain engineering process:  
1. _Scoping and Asset Identification_  
The process begins by defining the scope of the ML product line and identifying reusable core assets. This includes determining common tasks (e.g., classification, regression, anomaly detection) and capturing variability in datasets, models, pipelines, and hyperparameters. The variability space is defined by identifying configurable features, such as data sources, preprocessing techniques, and model architectures. The output of this stage is a clear understanding of shared and variable elements within the product line.
2. _Variability Modeling_  
Once the scope is defined, variability is modeled to capture differences across core assets. Variability models for ML include options for datasets (e.g., formats, augmentation strategies), model configurations (e.g., neural network architectures, hyperparameters), and pipeline workflows (e.g., preprocessing, training, and inference steps). These models provide a blueprint for systematically managing and reusing ML assets.
3. _Core Asset Development_  
This stage involves creating reusable ML core assets that form the foundation of the product line, and which have been described in the previous section.
4. _Traceability Establishment_  
Traceability enables systematic updates and ensures consistency throughout the lifecycle. It is supported by establishing explicit links between requirements, core assets, and their configurations using a combination of metadata tagging, feature models, and version control systems. Tools such as Enterprise Architect and IBM DOORS are employed to define and maintain these links at both system and ML-specific levels. Additionally, integration  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Core Asset Type** & **Low Readiness** & **Medium Readiness** & **High Readiness** \\ \hline Datasets & Raw data collected from sensors, APIs, or databases. & Labeled and cleaned datasets with minimal preprocessing. Partially trained or generic models (e.g., ResNet without fine-tuning). & Preprocessed, augmented, and versioned datasets. \\ \multirow{3}{*}{Models} & Initial model architectures (e.g., CNN, RNN). & Partially trained or generic models (e.g., ResNet without fine-tuning). & Pretrained and fine-tuned models for specific tasks. \\ \multirow{3}{*}{Pipelines} & Basic workflows for cleaning and normalization. & Configurable pipelines for training or evaluation. & Fully automated pipelines integrating training, evaluation, and deployment. Optimized configurations tailored to specific applications. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Asset Readiness Level for the ML Core Assets.  
with versioned repositories and model registries (e.g., Git, MLflow) ensures that changes in datasets, models, or pipelines can be traced back to feature selections and system-level requirements. This bidirectional traceability supports impact analysis, facilitates audits, and improves maintainability across evolving ML product lines.