전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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신경망 자동생성 지원 MLOps 기술 동향

MLOps Technology Trend Supporting Automatic Generation of Neural Network

  • 김선태 (AI컴퓨팅시스템SW연구실) ;
  • 조창식 (AI컴퓨팅시스템SW연구실)
  • S.T. Kim ;
  • C.S. Cho
  • 발행 : 2024.10.01
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초록

As more devices are used across various industries and their performance improves, artificial intelligence applications are being increasingly adopted. Hence, the rapid development of neural networks suitable for diverse devices can determine the competitiveness of companies. Machine learning operations (MLOps), which constitute a framework that supports neural network generation and its immediate application to devices, have become necessary for the development of artificial intelligence. Currently, most MLOps are provided by major companies such as Google, Amazon, and Microsoft, which provide cloud services supported by large-scale computing power. In addition, various services are provided by the open-source project Kubeflow. We examine basic concepts and technology trends in MLOps and unveil additional functions required in industry.

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과제정보

본고는 2023년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임[NO. 2021-0-00766, 신경망 응용 자동생성 및 실행환경 최적화 배포를 지원하는 통합개발 프레임워크 기술개발].

참고문헌

  1. Market and Market, "MLOps Market by Component (Platform and Services), Deployment Mode (Cloud and On-premises), Organization Size (Large Enterprises and SMEs), Vertical (BFSI, Healthcare and Life Sciences, Retail and eCommerce, Telecom) and Region - Global Forecast to 2027," 2022, https://chosareport-korea.com/mnmtc8518/
  2. Google, "Google Vertex AI," https://cloud.google.com/vertex-ai?hl=ko
  3. Microsoft, "MS Azure Machine Learning," https://azure.microsoft.com
  4. Amazon, "Amazon SageMaker," https://aws.amazon.com/pm/sagemaker
  5. Kubeflow Homepage, https://www.kubeflow.org/
  6. ETRI, "TANGO Project," GitHub, https://github.com/MLTANGO/TANGO
  7. 김선태, 조창식, "디바이스 적응형 신경망 생성 및 배포 구현," 전자공학회논문지, 제61권 제1호, 2024, pp. 27-33.
  8. C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object Detectors," arXive preprint, Jul. 2022, https://doi.org/10.48550/arXiv.2207.02696
  9. I. Shin, C. Cho, and S.-T. Kim, "Method for Expanding Search Space With Hybrid Operations in DynamicNAS," IEEE Access, vol. 12, 2024, pp. 10242-10253.
  10. J. Auh, C. Cho, and S.-T. Kim, "Improved contrastive learning model via identification of false-negatives in self-supervised learning," ETRI J., online published, 2024, https://doi.org/10.4218/etrij.2023-0285