SEAML: A Machine Learning Model Lineage Platform (Phase Ia)

现代软件应用严重依赖大数据和机器学习，要么作为基本用例的核心资产，要么作为支持基础设施。不同国家制定的隐私和安全法限制了如何以负责任的方式使用这些数据。为了解决社会问题，负责任地使用数据成为机器学习密集型系统的关键要求。当数据在利益相关者之间共享时，管理数据变得更具挑战性，利益相关者希望在数据使用和机器学习模型生命周期方面分担责任。本项目提出了一个安全、可审计的数据和ML模型沿袭平台（SEAML）。seam有一个通用的目的，但是它可以使基于电子合同定义数据和建模沿路和使用的利益相关者的生态系统受益最多。seam将区块链用于数据、模型和操作的元数据存储，并作为一种机制来定义数据和模型操作的不可抵赖契约。该平台具有数据工作流定义和执行引擎，用户可以在该引擎中指定数据源、对数据的操作顺序、对数据和操作的访问策略。元数据被定义为包含数据、模型和操作的超链接的记录，它使用分布式标识符、DIDin以及其他数据特征（如哈希码）来唯一标识数据。通过私钥/公钥控制对真实数据和模型的访问。

Modern software applications rely heavily on big data and machine learning, either as a core asset of their basic use cases or as supporting infrastructure. Privacy and security laws mandated by different countries constrain how the data can be used in an accountable manner. To address social concerns, responsible use of data becomes a key requirement for Machine Learning intensive systems. Governing data becomes even more challenging when it is shared among stakeholders who want a shared responsibility on data usage and machine learning models life cycle. This project proposes a secure and auditable data and ML models lineage platform (SEAML). SEAML has a general purpose, but it can benefit the most an ecosystem of stakeholders that define data and model lineage and usage based on electronic contracts. SEAML uses blockchain for data, models and operations' metadata storage, and as a mechanism to define non-repudiable contracts on data and model operations. The platform has a data workflow definition and execution engine where the user specifies the data sources, the sequence of operations on data, the access policies on data and operations. Metadata, defined as records containing hyper links to data, models and operations, uses distributed identifiers, DIDin addition to other data characteristics such as hash codes to uniquely identify data. Access to real data and to models is controlled through private/public keys.