Trained Model: The final, fully trained model file(s) in the required format (e.g., .h5, .pth, .sav, etc.). Model Architecture Documentation: Detailed explanation of the architecture, including the layers, parameters, hyperparameters, and techniques used. Training Notebooks or Scripts: The code used to train the model, including preprocessing, training, validation, and testing steps. Evaluation Metrics Report: A report showcasing the performance of the model, including metrics (e.g., accuracy, F1-score, precision, recall, AUC-ROC) and comparisons against benchmarks. Hyperparameter Tuning Logs: Details of the hyperparameter tuning process and the final set of optimal parameters.

Preprocessed Data Pipelines: Scripts or tools used to clean, preprocess, and augment the data. Training and Testing Data Splits: Information or files showing how the data was split into training, validation, and testing sets. Feature Engineering Documentation: Any derived features, transformations, or selection methods used for the model. Data Dictionary: Explanation of the dataset and the meaning of each column or feature.