AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration

AI 中文解读

AIGen让AI系统“零件清单”可以自动生成，就像给AI产品开了一份详细的说明书，确保每一步都合规透明。以往，开发AI需要记录用了哪些数据、模型权重、训练流程等，但缺少能自动生成标准格式的工具。现在，AIGen通过整合MLflow机器学习平台和大语言模型，能自动识别并生成符合国际标准SPDX 3.0的AI物料清单，就像工厂流水线自动贴标签一样。它还支持插件扩展，兼容PyTorch、TensorFlow等主流框架，开发者无需改动核心代码就能定制自己的采集规则。这项技术直接影响到每个使用AI的人。比如，欧洲《人工智能法案》要求高风险AI应用必须公开其组件来源，AIGen能帮企业快速合规，避免巨额罚款。对普通用户来说，这意味着未来用到的AI服务会更安全、可追溯——比如银行风控AI用了哪些数据训练、是否有偏见，都能一目了然。开发者也能用这些标准化清单排查漏洞，更像“AI的质检报告”，让技术真正变得可靠、可问责。

arXiv:2607.26652v1 Announce Type: new 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