# Gypscie: A Cross-Platform AI Artifact Management System

arXiv: https://arxiv.org/abs/2604.10311 · HTML: https://mlsanity.com/paper/2604.10311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 11 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting](https://mlsanity.com/paper/1912.00134/) (2019)
- [SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle](https://mlsanity.com/paper/1909.02976/) (2019)
- [MLModelCI: An Automatic Cloud Platform for Efficient MLaaS](https://mlsanity.com/paper/2006.05096/) (2020)
- [KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics](https://mlsanity.com/paper/1610.09451/) (2016)
- [Management of Machine Learning Lifecycle Artifacts: A Survey](https://mlsanity.com/paper/2210.11831/) (2022)
- [Helix: Holistic Optimization for Accelerating Iterative Machine Learning](https://mlsanity.com/paper/1812.05762/) (2018)
- [Workflow Provenance in the Lifecycle of Scientific Machine Learning](https://mlsanity.com/paper/2010.00330/) (2020)
- [RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines](https://mlsanity.com/paper/2506.03401/) (2025)
- [Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications](https://mlsanity.com/paper/2502.00808/) (2025)

## Abstract (arXiv)

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.