AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration
Abstract
The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.g., datasets, model weights, training pipelines, and runtime dependencies. Although the Software Package Data Exchange (SPDX) 3.0 standard introduced native support for AI and dataset profiles, practical tooling capable of generating standards-compliant AI Bills of Materials (AIBoMs) in an automated and extensible manner remains scarce. This paper presents AIGen, a modular AIBoM generator that produces machine-readable, interoperable inventories of AI system components that comply with the SPDX 3.0 AI profile. AIGen works on top of the MLflow MLOps framework and combines mining heuristics with Large Language Models to generate AIBoMs. A plugin interface allows practitioners to extend the tool with domain-specific collectors without modifying the core codebase, supporting heterogeneous AI frameworks such as Hugging Face, PyTorch, and TensorFlow. AIGen is designed to facilitate compliance with the European Union AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001, providing a concrete, reusable foundation for transparent, accountable AI supply chain governance. Tool URL: https://github.com/danielebifolco/AIGen Tool Video: https://youtu.be/\_nAbXDWfVL4
Cite
@article{arxiv.2607.26652,
title = {AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration},
author = {Federica Pepe and Daniele Bifolco and Costantino Martignetti and Aureliano D'Amici and Fabiano Izzo and Damian A. Tamburri and Massimiliano Di Penta},
journal= {arXiv preprint arXiv:2607.26652},
year = {2026}
}