Gypscie: A Cross-Platform AI Artifact Management System
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 interactin...
| Authors | Fabio Porto, Eduardo Ogasawara, Gabriela Moraes Botaro et al. |
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| Primary category | cs.AI |
| Published | 11 April 2026 |
| Last updated at source | 6 July 2026 |
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