## 4 Requirements for Product Line Engineering for Machine Learning  
Given the benefits of PLE, we could try to adopt the PLE lifecycle process as it is currently defined. Yet, this conventional process cannot be adopted as-is for ML due to the different and unique characteristics and lifecycle of ML systems. While the core principles of PLE--such as defining core assets, managing variability, and separating domain and application engineering--are relevant, several requirements must be addressed:  
1. _Data as a Core Asset_  
PLE must expand its scope to treat ML core assets as fundamental components of the product line, alongside traditional software components and architectures. This includes systematic management of ML-specific assets such as datasets, model architectures, feature pipelines, and hyperparameters. Processes must account for the iterative nature of these assets, ensuring traceability and version control to handle updates effectively. Comprehensive management of variability, configurations, and dependencies across these assets is essential to maintaining consistency, enabling reuse, and supporting the dynamic and evolving requirements of ML systems. 2. _Supporting Dynamic Evolution_  
While traditional SE PLE assets, such as requirements, architecture models, and test cases, are inherently dynamic and subject to change over time, ML-specific assets such as datasets, trained models, and hyperparameters can often evolve with even greater frequency. This is primarily due to ongoing data acquisition, shifting distributions, and feedback-driven retraining cycles intrinsic to many ML applications. SE assets typically evolve due to changes in stakeholder needs or system requirements, whereas the dynamics of the data and model performance feedback drive ML assets. The ML PLE framework needs to account for both types of evolution by supporting traceability and reuse across interdependent assets. 3. _Incorporating Feedback Loops_  
PLE for ML must integrate continuous feedback mechanisms from deployed systems into its lifecycle. Feedback loops are vital for capturing real-world performance data, identifying issues such as concept drift, and triggering updates to datasets, models, and pipelines. These feedback-driven updates ensure that the product line evolves in response to operational needs and maintains optimal performance. 4. _Enabling Pipeline-Centric Workflows_  
PLE processes must adapt to the pipeline-centric nature of ML development. Automated workflows for data preprocessing, model training, evaluation, and deployment must be treated as modular and configurable assets. The PLE framework should support the design, reuse, and customization of these pipelines to streamline development and deployment across the product line. Modularity and automation in pipeline management are essential to reducing manual effort and enhancing scalability. 5. _Addressing Testing and Validation Complexity_  
PLE for ML must expand testing and validation processes to include the unique complexities of ML systems. This involves testing not only software functionality but also data quality, model performance, and pipeline correctness. The inherent uncertainties in data and algorithms require rigorous validation protocols, including checks for data consistency, model robustness, and the integrity of automated workflows. These processes must ensure that product variants meet their requirements and perform reliably in real-world conditions. 6. _Adjusting the Production Plan for ML_  
A dynamic production plan is required to accommodate the iterative and evolving nature of ML systems. This includes continuous data collection, preprocessing, and augmentation to support retraining and updates. Retraining workflows should be triggered by feedback loops or changes in data distributions. Flexible pipeline infrastructures must enable updates without disrupting production systems. Continuous monitoring systems should be integrated to detect performance degradation, initiate updates, and maintain the reliability of deployed ML solutions. 7.