Government

[Korean National Police Agency] Building a Field-ready AI Analytics Platform Integrated with the Big Data Platform

Integrated with the Korean National Police Agency's big data platform, we built a unified analytics environment for analyzing public safety data and developing AI models. By providing the capabilities needed for data analytics and AI in one environment, we laid the groundwork for field-oriented use of data.

CheetahMLOps

A Field-oriented Big Data and AI Analytics Environment

The need to analyze and use the diverse public safety data accumulated in police work more effectively had grown. The agency therefore aimed to strengthen practical data analytics skills and infrastructure so that frontline officers could work with data directly.

Enabling Hands-on AI Training for Frontline Officers and Sharing of Analytics Models

The key challenge was to provide an environment where many users could access and practice simultaneously, and to connect field data from the existing big data platform so standard analytics models could be used in real work.

  • Large-scale training environment: An environment where up to 120,000 officers can take basic programming courses and practice with models

  • Sharing of standard analytics models: A way to share analytics models so non-specialists can practice with field data

  • Integration with the existing platform: A framework to use public safety data accumulated on the existing big data platform for practice and analysis

Building a Big Data and AI Analytics Platform for Field Use

With Cheetah, we provided a lecture and hands-on environment for sharing training materials and standard analytics models, and enabled many users to practice simultaneously in independent analytics environments using field data connected from the existing big data platform.

  • Lecture and hands-on features: An environment for sharing training materials and standard analytics models that non-specialists can easily practice with

  • Concurrent analytics environments: Hundreds of users can access the platform at once and set up independent analytics environments

  • Integration with the existing big data platform: Field data such as community safety, traffic, and investigation data can be used for practice and analysis

Greater Use of Public Safety Data and Faster Organizational Innovation

By sharing standard analytics models with frontline units, the agency established a system where each office can set up its own analytics and practice environment and analyze field data. By connecting to community safety, traffic, and investigation data collected on the existing big data platform, frontline officers can now practice with analytics models on real data and apply them to their work.

  • Greater field analytics capability: Frontline officers can analyze and practice directly with field data from the existing big data platform

  • Foundation for scaling training: A system that runs from basic training to hands-on practice with standard analytics models

  • Faster organizational innovation: Supports the shift to science-based policing by spreading AI and data-driven ways of working

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