Education & Research

[Kookmin University] Adopting an AI Platform that Connects Hands-on Practice to Operations

To let students continue AI development practice seamlessly in its Business IT convergence curriculum, Kookmin University adopted a cloud-based integrated AI development platform that supports concurrent practice, GPU use, and infrastructure management.

CheetahMLOps

A Sustainable AI Practice and Operating Environment

A practical curriculum combining business and IT required an AI development lab environment where students could solve real-world problems. It also needed a foundation where many students could practice independently at the same time, keep their development history, and use GPUs and infrastructure efficiently.

Ensuring Continuity in AI Practice and Efficient Resource Operations

The key challenge was to let many students practice AI independently at the same time while securing both development continuity and efficient resource use.

  • Persistent lab environment: A foundation for keeping development environments and work history to continue research and practice

  • Concurrent independent practice: An environment where many students can access the platform at once and practice independently

  • GPU and infrastructure management: A framework to efficiently operate GPU resources and infrastructure within a limited budget

Providing a Cloud-based Integrated AI Development Platform

Through a cloud-based platform, we provided persistent AI development environments and a multi-tenant lab environment, with integrated support for GPU and infrastructure operations.

  • Persistent development environments: A lab foundation for saving and reusing development environments on the platform

  • Multi-tenant lab environment: An environment where many users can access the platform at once and develop independently

  • Integrated GPU and infrastructure operations: Usage-based GPU use and infrastructure management on the platform

Continuity and Efficiency in AI Education and Practice

Students can now practice AI independently at the same time with just an internet connection, and save and reuse their development environments to keep learning. Simplified course and lab management also lets faculty focus more on improving the quality of education.

  • Concurrent lab environment: Many students can carry out independent AI development practice at the same time

  • Development continuity: Continuous practice by keeping and reusing development environments and data

  • More efficient course operations: Simpler example sharing and lab operations reduce the teaching workload

  • Are you curious about an enterprise AI integration platform? → Cheetah

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