ETRI Open-Sources TANGO: No-Code MLOps for AI Development

ETRI (Electronics and Telecommunications Research Institute) has released TANGO as open source, providing a no-code MLOps framework that automates neural network generation and deployment. The tool addresses the growing shortage of AI specialists by making machine learning accessible to non-experts.

No-Code Approach

TANGO enables users to develop AI applications without writing code, automating the complex pipeline from model design to deployment. This democratization of AI development allows domain experts to create machine learning solutions without deep technical expertise in neural network architecture.

Hardware Flexibility

The framework supports deployment across various hardware environments, from edge devices to cloud infrastructure. This flexibility means models developed in TANGO can be optimized for specific deployment targets without manual reconfiguration.

Addressing the Talent Gap

With demand for AI specialists far outpacing supply, tools like TANGO are essential for organizations looking to leverage machine learning. By reducing the expertise barrier, ETRI enables more organizations to participate in AI development without competing for scarce ML engineering talent.

Open Source Release

Making TANGO open source allows the global community to contribute improvements and adaptations. This collaborative approach accelerates development while ensuring the tool remains accessible to organizations of all sizes.