Custom machine learning model trained on your dataset. Deep learning model (CNN, RNN, Transformer, etc.) for specific tasks. Preprocessing pipeline for feature extraction and cleaning. Trained model saved in .pkl, .pt, or .h5 format with documentation.
Image classification, object detection, or segmentation model NLP model for classification, summarization, or Q&A Annotated dataset with training pipeline for reproducibility
Model evaluation using metrics (accuracy, precision, recall, F1-score, AUC). Confusion matrix, ROC curves, and explainability tools (e.g., SHAP, LIME). PDF/HTML report summarizing performance, methodology, and insights.