Meeting of the ITU Focus Group on Machine Learning for Future Networks including 5G

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Meeting of the ITU Focus Group on Machine Learning for Future Networks including 5G

The ITU-T Focus Group on Machine Learning for Future Networks including 5G was established by ITU-T Study Group 13 at its meeting in Geneva, 6-17 November 2017. The Focus Group will draft technical reports and specifications for machine learning (ML) for future networks, including interfaces, network architectures, protocols, algorithms and data formats.

Speaker(s)
  • Charles Chike Asadu
    Focus Group Vice-Chairman
    University of Nigeria
  • Profile picture of Qiang Cheng
    Qiang Cheng
    Focus Group Vice-Chairman
    China Academy of Information and Communications Technology (CAICT)
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    Speaker abstract

    The Role of MLOps in Enabling Successful ML Deployments in the Telecommunications Industry; The telecommunications industry is increasingly relying on machine learning (ML) to improve network performance, reduce costs, and enhance customer experience. However, deploying ML models in this industry comes with its own set of challenges, including the need for low-latency processing, distributed architectures, and the lack of standardization. MLOps is an emerging discipline that applies DevOps practices to ML development and deployment, aiming to address these challenges and enable successful ML deployments. We will discuss the motivations for MLOps, its benefits, and its role in enabling successful ML deployments in the telecommunications industry. We will explore the challenges of ML deployments in telecommunications, the evolution of ML workflows in this industry from the ML Function Orchestrator (MLFO), which was proposed by the ITU focus group on “Machine Learning for Future Networks including 5G (FG-ML5G)”, to pipelining and MLOps, and the benefits of MLOps in terms of improved model quality, faster time-to-market, and increased collaboration between teams. We will also discuss the specific challenges that 5G and 6G networks present for ML deployments in telecommunications, and how MLOps can help overcome them. Finally we will be highlighting the importance of MLOps in enabling successful ML deployments in the telecommunications industry, and the need for telecommunications companies to adopt MLOps practices to stay competitive in the ever-evolving telecommunications landscape.
    Salih Ergut
    Chief Data Science, R&D, and Strategy Officer
    OREDATA
  • Seongbok Baik
    Focus Group Vice-Chairman
    KT
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