Korean researchers have been quietly empowering industries – from factories to hospitals and shipyards – with accessible AI tools since 2021, and now they’ve opened the doors even wider.
The Electronics and Telecommunications Research Institute (ETRI) has released the core technology behind its machine learning operations (MLOps) tool as open source on GitHub. This tool automatically generates neural networks and streamlines the deployment process, eliminating much of the traditional coding headache.
On November 6, 2025, the ETRI team hosted its fourth public seminar at the Science and Technology Center in Gangnam-gu, Seoul, to cultivate a thriving community around the TANGO framework. TANGO automatically develops and optimally deploys AI-powered software across diverse hardware environments, including cloud platforms, Kubernetes, and on-device systems.
The Hurdles of AI Implementation
Applying artificial intelligence isn’t always straightforward, even when the problem seems simple. For instance, identifying defects in steel during quality control is visually easy for a human inspector, but translating that skill into an AI system proved challenging. Similarly, doctors can readily diagnose tuberculosis from X-ray images, yet building an automated AI prediction model for the same task has been difficult.
The TANGO framework is designed for domain experts – those with deep knowledge of their field – who may lack extensive neural network expertise. Its user-friendly design allows for quick installation via a simple command and immediate operation through a web interface.
Bridging the AI Skills Gap
Traditionally, developing AI applications involved a division of labor: domain experts focused on data labeling, while software developers handled model development, training, and deployment. However, the rapid expansion of artificial intelligence is creating a surge in demand for AI and software solutions across all sectors.
This demand is outpacing the available pool of qualified AI and software specialists. To address this, ETRI has developed an automated neural network algorithm optimized for object recognition, tailored to the needs of domestic industries, and has now made it publicly available. The institute has also released LLMOps tools to support the development of generative AI.
The research team plans to release updated versions of the source code on GitHub every six months and will continue to host annual public seminars to share both the technology and practical insights. Over four sessions, a total of 944 individuals from 552 institutions have participated in the TANGO seminars, exchanging knowledge and fostering collaboration.
More information:
On GitHub: github.com/ML-TANGO/TANGO
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No-code machine learning development tools (2026, January 2)
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