ITU AI/ML in 5G Challenge: Radio Link Failure Prediction Challenge

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ITU AI/ML in 5G Challenge: Radio Link Failure Prediction Challenge

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  • Stable and high-quality internet connectivity is mandatory to 5G mobile networks, but once something unexpected happens, the influence of the defect is quite severing. The talk will describe the problem ITU-ML5G-PS-036, Using weather info for radio link failure (RLF) prediction.  This problem is about how to  predict radio link failure using weather information and network KPIs. Then, the dataset will be described together with the goals of the challenge. Participants are required to create a Machine Learning model to pinpoint the network status of failures and mis-operation using the provided data sets and evaluate the performance of the developed model.

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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
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