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

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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.