Government

[Seongnam City Hall] Adopting a Cloud-based AI Development Platform for Hackathon Operations

To open its Traffic and Autonomous Driving Big Data Center and run a hackathon, Seongnam City adopted a cloud-based AI development environment where participants can quickly carry out AI projects using GPU resources and data.

CheetahMLOps

An AI Development Environment for Running a Public Big Data Hackathon

With the opening of its Traffic and Autonomous Driving Big Data Center, Seongnam City organized a hackathon to discover and promote AI-based ideas for using public big data. This required an AI development environment where participants could quickly carry out projects using GPU resources and data, without building their own infrastructure.

Securing Resources, Data, and Development Environments for the Hackathon

The key challenge was to let participants share data and build AI models in their own independent development environments within the short competition period.

  • GPU resources: GPU resources for participants to train and experiment with AI models during the hackathon

  • Data sharing environment: An environment to easily upload project data and share it among team members

  • Individual ML environments: A foundation for quickly setting up independent ML development environments with data for each student

Providing a Cloud-based AI Development Environment for the Hackathon

With cloud-based Cheetah, we provided GPU resources without any infrastructure installation, along with data sharing and ML environment setup, creating a ready-to-run hackathon environment.

  • Cloud-based GPU environment: GPU resources ready for hackathon participants to use immediately, with no infrastructure installation

  • Data sharing: Datasets and volumes on the platform support data upload and sharing among participants

  • Ready-to-use ML environment: An ML development environment for going straight from data upload to AI model implementation

More Efficient Hackathon Operations and a Foundation for AI Projects

Seongnam City was able to provide participants with an AI development environment during the hackathon without building separate infrastructure, and students could freely share data and carry out projects more efficiently. Participants could also quickly set up their own ML environments with the shared data and smoothly build and test AI models even within a short timeframe.

  • Minimal operating burden: A cloud-based environment for running the hackathon quickly without installing infrastructure

  • More efficient collaboration: Datasets and volumes make data sharing and collaboration among participants easy

  • Faster development: ML environments with shared data are ready within one minute, so projects move quickly

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