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Tae-Ho Kim

Tae-Ho Kim

Co-founder & CTO | Nota

Tae-Ho Kim is a founder and CTO of Nota. He received his bachelor’s degree in bio and brain engineering and his master’s degree in electrical engineering, both from KAIST. He has researched a variety of applications using deep learning, especially speech recognition, text to speech and object tracking. He has published fourteen papers and holds nine patents related to deep learning. Tae-Ho’s current responsibilities include researching and formulating the strategic direction of Nota’s NetsPresso hardware-aware MLOps tool for creating production-level AI models. He leads Nota’s R&D unit and manages the company’s technical partnerships with prestigious AI research labs.

Kim_T
Kim_T

Tae-Ho Kim

Co-founder & CTO | Nota

Tae-Ho Kim is a founder and CTO of Nota. He received his bachelor’s degree in bio and brain engineering and his master’s degree in electrical engineering, both from KAIST. He has researched a variety of applications using deep learning, especially speech recognition, text to speech and object tracking. He has published fourteen papers and holds nine patents related to deep learning. Tae-Ho’s current responsibilities include researching and formulating the strategic direction of Nota’s NetsPresso hardware-aware MLOps tool for creating production-level AI models. He leads Nota’s R&D unit and manages the company’s technical partnerships with prestigious AI research labs.

Session

Empower Your Edge Device Using NetsPresso – No AI Engineer Required!

Despite the outstanding achievements of deep learning models, commercializing deep learning applications remains challenging. AI implementation experts are rare, and AI models for production must meet demanding requirements, processing high-bandwidth data in real time with limited memory and processing resources, while preserving privacy. In this talk, we introduce NetsPresso, a hardware-aware MLOps tool for creating production-level AI models. Different NetsPresso modules support each step of the development of a deployable AI model, including data preparation, model selection, optimization and deployment. We’ll illustrate NetsPresso’s capabilities via use cases in intelligent transportation systems and driver monitoring systems.