Education & Research

[KAIST Graduate School of AI] Adopting an Integrated Platform for More Efficient AI R&D

KAIST Graduate School of AI adopted a cloud-based integrated AI development platform to flexibly scale GPU resources within a limited budget and conduct AI research anytime, anywhere.

CheetahMLOps

Flexible GPU Scaling and an AI Research Environment

KAIST Graduate School of AI needed an environment to quickly scale GPU resources within a limited budget for short-term projects. It also required a foundation to continue AI research regardless of location and easily use the libraries and storage suited to each research purpose.

Securing Efficient GPU Use and Accessible Research Environments

The key challenge was to use GPU resources efficiently within a limited budget while giving researchers instant access to the AI development environments they need, anytime and anywhere.

  • GPU scaling: An environment to flexibly scale GPU resources for short-term projects

  • Remote research environment: An environment for conducting AI research anytime, anywhere with just an internet connection

  • Development environment setup: An environment with default storage and a wide range of AI libraries and frameworks

Providing a Cloud-based Integrated AI Development Platform

With Cheetah, we provided a usage-based GPU environment and a remotely accessible research environment, along with default storage and version-specific AI libraries and frameworks.

  • Usage-based GPU environment: An environment to use platform GPU resources as needed and manage costs efficiently

  • Access-based research environment: An environment for conducting AI research instantly wherever there is internet access

  • Integrated storage and libraries: Default storage provided together with version-specific AI libraries and frameworks

More Efficient AI Research and Cost Savings

Researchers can now continue AI research regardless of location and use GPU resources flexibly while reducing costs. With default storage and version-specific libraries, they can quickly set up environments suited to their research and reliably carry out short-term projects.

  • Greater research accessibility: An environment for conducting AI research from anywhere, anytime

  • Cost savings: Lower costs than AWS instances through usage-based GPU use

  • Faster research environment setup: Quick setup of research environments with default storage and version-specific libraries

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

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