DLProv: a suite of provenance services for deep learning workflow analyses.

Deep learning (DL) workflows consist of multiple interdependent and repetitive steps, including data preparation, model training, evaluation, and deployment. Each step involves decisions impacting the final model's performance, interpretability, and applicability. These models rely on data, preprocessing operations, and configuration, underscoring the need for mechanisms to ease the analysis throughout the entire life cycle-from model generation and selection to deployment. Moreover, ensuring trust, reproducibility, and transparency becomes important as DL models transition into production environments. Traceability across the steps of the DL workflow is essential to address these challenges. However, existing traceability solutions often present limitations. Many fail to integrate the steps of the DL workflow, focusing on either data preparation or model training. Additionally, they frequently rely on proprietary formats to represent traceability data and rarely produce a provenance document that can accompany the model into production. To bridge these gaps, we present DLProv, a suite of provenance services designed to ensure end-to-end traceability across DL workflows. DLProv supports structured query language (SQL)-based querying during training and generates provenance graphs that capture data preparation steps, model training, and evaluation. These provenance graphs comply with the PROV