[KICE] Building a Cloud and DevOps Environment for an AI-based Academic Achievement Diagnosis System

As part of building the National Basic Academic Skills Support Portal, the Korea Institute for Curriculum and Evaluation (KICE) launched a project to build an AI-based academic achievement diagnosis system. Through this project, we built a multi-cloud infrastructure connecting KT Cloud and NHN Cloud, along with a DevOps operating environment, based on Serengeti CMP and PaaS.

A Cloud-native Operating Environment for AI-based Academic Diagnosis Services

KICE needed a cloud-native integrated infrastructure to reliably operate its AI-based academic achievement diagnosis system and the National Basic Academic Skills Support Portal. It also wanted a DevOps-based service operating framework that connects development, deployment, and operations, so it could continuously develop and deliver diverse learning content and diagnosis services.

Securing a Stable Foundation for Academic Diagnosis Services

KICE needed an environment that could reliably operate and continuously scale its AI academic diagnosis services.

  • Cloud-native environment: Cloud infrastructure for stable service operations

  • Multi-cloud management: Integrated management of KT Cloud and NHN Cloud resources

  • DevOps-based development and deployment: An automated environment to develop and deploy content and services quickly

Building an Operating Environment on Serengeti CMP and PaaS

We built an environment that provides integrated support for multi-cloud resource management and DevOps operations.

  • Multi-cloud CMP environment: Built an environment for integrated management of KT Cloud and NHN Cloud resources

  • GitOps-based development and deployment: Configured standard project templates and deployment pipelines

  • Service automation catalog: Automated repetitive service creation and operations

More Efficient Public Education Service Operations

By standardizing cloud operations and service deployment, KICE improved operational stability and development efficiency.

  • More efficient cloud operations: Improved efficiency in managing multi-cloud resources

  • Higher development and deployment productivity: Faster development with a standardized DevOps environment

  • Stable service operations: A scalable foundation for operating the academic diagnosis system

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