## 5 ML PLE Framework
### Metamodel
In addition to these general core assets, we have identified four different types of ML Core Assets, which are listed below:  
1. _Datasets_ Datasets are foundational to ML systems, as models learn from historical data to make predictions. Core dataset assets include the following:  
* Raw Datasets: collected from diverse sources unprocessed data. * Labeled Datasets: annotated data for supervised learning. * Preprocessed Datasets: cleaned, normalized, and transformed datasets prepared for specific tasks, such as removing outliers or scaling features. * Augmented Data Variants: variations generated through augmentation techniques, such as flipping, cropping, or noise addition, to improve model generalization. * Metadata and Documentation: information about data sources, labeling processes, and preprocessing steps to ensure traceability and reproducibility. 2. _Models_ Models represent the algorithms and architectures used to solve ML problems. Core model assets include the following:  
* Reusable Architectures: standardized designs for ML models, such as convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence data, or transformers for natural language tasks. * Pretrained Models: general-purpose models trained on large datasets (e.g., ResNet, BERT, GPT) that can be fine-tuned for specific tasks, significantly reducing training time and data requirements. * Model Templates: configurable model blueprints that support different hyperparameters, layer sizes, or activation functions for customization. * Model Versions: trained and validated models stored with metadata, such as training dataset, performance metrics, and parameter settings. * Explainability Modules: components to interpret model predictions, essential for domains requiring transparency (e.g., healthcare, finance). * _Pipelines_ Pipelines automate the end-to-end ML workflow, ensuring consistency and efficiency. Core pipeline assets include the following:
* Data Processing Pipelines: workflows for cleaning, transforming, and augmenting raw data, adaptable for different datasets or domains. * Training Pipelines: automated processes for training models, including dataset splitting (training, validation, test), batching, and distributed training. * Inference Pipelines: deployment workflows that include preprocessing input data, running model predictions, and post-processing outputs. * Evaluation Pipelines: configurable workflows to assess model performance using metrics such as accuracy, F1-score, precision, recall, or mean squared error. * Monitoring Pipelines: tools to track deployed model performance, detect drift in data distributions, and trigger retraining when necessary. * _Hyperparameters_ Hyperparameters are settings that influence how ML models learn during training [20]. Core hyperparameter assets include the following:
* Default Hyperparameter Configurations: predefined settings for common ML models, such as learning rate, batch size, and number of epochs. * Tunable Ranges: variability definitions for hyperparameters, enabling systematic exploration through grid search, random search, or Bayesian optimization. * Hyperparameter Templates: task-specific configurations for optimizing performance, such as low-latency setups for real-time applications or high-accuracy settings for critical tasks. * Optimization Records: documentation of past experiments, including tested hyperparameter values and their corresponding model performance metrics, to guide future tuning efforts. Within the ML PLE process, reuse techniques such as transfer learning, modular feature pipelines, and architectural patterns (e.g., encoder-decoder backbones) are thus also treated as reusable core assets. These can be captured in the core asset base and systematically managed through variability models and asset readiness levels, enabling the structured reuse of assets across multiple ML product variants.