Gypscie: A Cross-Platform AI Artifact Management System
🤖 AI Summary
This work addresses the inefficiencies in cross-platform development and deployment of AI systems caused by the lack of a unified abstraction for heterogeneous AI artifacts—such as datasets, models, and data pipelines—throughout their lifecycle. We propose the first AI artifact management system that integrates knowledge graphs with rule-based reasoning to semantically model diverse artifacts in a unified manner. The system introduces a rule-driven query language enabling joint inference over data and models, and abstracts lifecycle operations into schedulable high-level dataflows. Built-in provenance tracking ensures explainability, while an automated optimization and scheduling engine translates abstract specifications into efficient executions across servers, cloud, and supercomputing environments. Experimental results demonstrate that our approach significantly outperforms existing solutions in both functional coverage and scheduling efficiency.
Application Category
📝 Abstract
Artificial Intelligence (AI) models, encompassing both traditional machine learning (ML) and more advanced approaches such as deep learning and large language models (LLMs), play a central role in modern applications. AI model lifecycle management involves the end-to-end process of managing these models, from data collection and preparation to model building, evaluation, deployment, and continuous monitoring. This process is inherently complex, as it requires the coordination of diverse services that manage AI artifacts such as datasets, dataflows, and models, all orchestrated to operate seamlessly. In this context, it is essential to isolate applications from the complexity of interacting with heterogeneous services, datasets, and AI platforms. In this paper, we introduce Gypscie, a cross-platform AI artifact management system. By providing a unified view of all AI artifacts, the Gypscie platform simplifies the development and deployment of AI applications. This unified view is realized through a knowledge graph that captures application semantics and a rule-based query language that supports reasoning over data and models. Model lifecycle activities are represented as high-level dataflows that can be scheduled across multiple platforms, such as servers, cloud platforms, or supercomputers. Finally, Gypscie records provenance information about the artifacts it produces, thereby enabling explainability. Our qualitative comparison with representative AI systems shows that Gypscie supports a broader range of functionalities across the AI artifact lifecycle. Our experimental evaluation demonstrates that Gypscie can successfully optimize and schedule dataflows on AI platforms from an abstract specification.
Problem
Research questions and friction points this paper is trying to address.
AI artifact management
cross-platform
model lifecycle
heterogeneous services
unified view
Innovation
Methods, ideas, or system contributions that make the work stand out.
cross-platform
knowledge graph
AI artifact management
dataflow scheduling
provenance tracking
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