Government

[KFTC] Building an LLMOps Platform for More Efficient LLM Service Operations

The Korea Fair Trade Commission (KFTC) adopted Cheetah to operate LLM services built on its Fairdata platform, creating an AI development and serving environment that can repeatedly run everything from data preprocessing to model training and redeployment.

CheetahLLMOps

A Training and Redeployment Framework for LLM Service Operations

KFTC had built services by fine-tuning LLMs through its Fairdata platform and needed a follow-on platform to continuously improve and reliably operate them. In particular, it required an integrated framework that analysts and modelers could use together in an environment where data preprocessing, model training, and redeployment repeat continuously.

Automating LLM Training and Securing Reliable Service Deployment

The key challenge was to provide a development environment for both big data analytics and LLM research, and to efficiently handle repetitive preprocessing and large-scale model deployment.

  • LLM operating environment: An integrated environment to develop, train, and deploy LLMs

  • Automated LLM data preprocessing: A framework to reliably repeat periodic preprocessing of LLM training data

  • Large-scale LLM deployment: An operating foundation to quickly deploy and redeploy large language models (LLMs) to production

Building an Integrated Platform that Automates LLM Training and Deployment

With Cheetah, we provided a development environment for data analytics and model research and built an LLM service operating framework that combines workflow-based preprocessing and deployment automation with one-click serving.

  • Automated preprocessing workflows: Automated training data preprocessing with Cheetah workflows, minimizing repetitive work

  • Large model deployment: Supports storage of large model files and one-click deployment, with automatic endpoint creation for fast service integration

  • Deployment automation for operations: Established a workflow-based model redeployment process to run services and update models efficiently

More Efficient LLM Service Operations and Faster Redeployment

With Cheetah, KFTC now has an automated framework to repeatedly run the data preprocessing and model redeployment needed for fine-tuning. This has made LLM service operations more efficient and created a foundation for reflecting model improvements in services more quickly.

  • Automated repetitive work: Automated repetitive tasks such as data preprocessing and model redeployment, improving operational efficiency

  • Faster service updates: Simplified model deployment so improved models reach services faster

  • Stronger operational stability: Standardized the training and deployment framework for LLM services, securing a stable operating foundation

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

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