Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Operationalizing AI in Future Networks: A Bird's Eye View from the System Perspective

Qiong Liu, Tianzhu Zhang, Masoud Hemmatpour, Han Qiu, Dong Zhang, Chung Shue Chen, Marco Mellia, Armen Aghasaryan

TL;DR

This article enumerate the key factors hindering the integration of AI/ML in real networks, and review existing solutions to uncover the missing components, and highlights a promising direction, that is, machine learning operations (MLOps), that can close the gap.

Abstract

Modern Artificial Intelligence (AI) technologies, led by Machine Learning (ML), have gained unprecedented momentum over the past decade. Following this wave of "AI summer", the network research community has also embraced AI/ML algorithms to address many problems related to network operations and management. However, compared to their counterparts in other domains, most ML-based solutions have yet to receive large-scale deployment due to insufficient maturity for production settings. This article concentrates on the practical issues of developing and operating ML-based solutions in real networks. Specifically, we enumerate the key factors hindering the integration of AI/ML in real networks and review existing solutions to uncover the missing considerations. Further, we highlight a promising direction, i.e., Machine Learning Operations (MLOps), that can close the gap. We believe this paper spotlights the system-related considerations on implementing \& maintaining ML-based solutions and invigorate their full adoption in future networks.

Operationalizing AI in Future Networks: A Bird's Eye View from the System Perspective

TL;DR

This article enumerate the key factors hindering the integration of AI/ML in real networks, and review existing solutions to uncover the missing components, and highlights a promising direction, that is, machine learning operations (MLOps), that can close the gap.

Abstract

Modern Artificial Intelligence (AI) technologies, led by Machine Learning (ML), have gained unprecedented momentum over the past decade. Following this wave of "AI summer", the network research community has also embraced AI/ML algorithms to address many problems related to network operations and management. However, compared to their counterparts in other domains, most ML-based solutions have yet to receive large-scale deployment due to insufficient maturity for production settings. This article concentrates on the practical issues of developing and operating ML-based solutions in real networks. Specifically, we enumerate the key factors hindering the integration of AI/ML in real networks and review existing solutions to uncover the missing considerations. Further, we highlight a promising direction, i.e., Machine Learning Operations (MLOps), that can close the gap. We believe this paper spotlights the system-related considerations on implementing \& maintaining ML-based solutions and invigorate their full adoption in future networks.
Paper Structure (17 sections, 5 figures, 1 table)

This paper contains 17 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Landing AI in networks
  3. Operationalizing AI/ML in production networks: the status quo
  4. MLOps: towards end-to-end pipelines
  5. Conclusion

Figures (5)

  • Figure 1: Basic components for real-world ML systems (Picture originated from Sculley et al. sculley2015hidden).
  • Figure 2: ML lifecycle in production settings.
  • Figure 3: Conventional vs. Automated ML lifecycle.
  • Figure 4: Operationalizing AI/ML in Future Networks: A Bird's Eye View from the System Perspective
  • Figure 5: The benefits of MLOps for KPI prediction