Gypscie: A Cross-Platform AI Artifact Management System

Gypscie is a new cross-platform AI artifact management system that unifies the complexity of managing machine learning models across diverse infrastructure through a knowledge graph and rule-based query language. The system streamlines the entire AI model lifecycle—from data preparation through deployment and monitoring—while enabling explainability through provenance tracking.

Gypscie addresses a critical pain point in modern AI development: the fragmentation of tools, platforms, and services required to manage machine learning artifacts. Traditional AI workflows force developers to navigate heterogeneous ecosystems spanning data storage systems, model registries, compute platforms, and monitoring tools. This research paper presents a unified abstraction layer that simplifies these interactions while maintaining flexibility across deployment environments.

The system's architecture relies on a knowledge graph to capture semantic relationships between datasets, models, and dataflows, enabling intelligent reasoning and optimization. By exposing a rule-based query language rather than platform-specific APIs, Gypscie reduces cognitive overhead and accelerates development cycles. The provenance tracking capability directly addresses reproducibility and explainability concerns that regulators and enterprises increasingly demand.

For the AI development community, this represents incremental progress toward reducing operational friction rather than a breakthrough innovation. The platform enables developers to specify abstract dataflow definitions that automatically optimize and schedule across heterogeneous infrastructure—whether on-premises servers, cloud platforms, or supercomputers. This portability reduces vendor lock-in and simplifies multi-cloud strategies.

The broader significance lies in democratizing AI lifecycle management for organizations lacking dedicated MLOps infrastructure. As AI models become more central to business operations, tooling that abstracts away infrastructure complexity gains strategic value. Future developments should focus on practical adoption rates, community contribution, and integration depth with existing enterprise platforms to determine real-world impact.

• →Gypscie unifies AI artifact management across multiple platforms through a knowledge graph and rule-based query language, reducing operational complexity.
• →The system automates optimization and scheduling of model lifecycle dataflows across diverse infrastructure without platform-specific reconfiguration.
• →Provenance tracking enables explainability and reproducibility, addressing regulatory and enterprise governance requirements.
• →Cross-platform portability reduces vendor lock-in and simplifies multi-cloud and hybrid deployment strategies.
• →The abstraction layer democratizes advanced MLOps capabilities for organizations without dedicated infrastructure expertise.