# Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions

arXiv: https://arxiv.org/abs/2410.18793 · HTML: https://mlsanity.com/paper/2410.18793/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Large Generative AI Models for Telecom: The Next Big Thing?](https://mlsanity.com/paper/2306.10249/) (2023)
- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [Open RAN meets Semantic Communications: A Synergistic Match for Open, Intelligent, and Knowledge-Driven 6G](https://mlsanity.com/paper/2310.09951/) (2023)
- [SliceOps: Explainable MLOps for Streamlined Automation-Native 6G Networks](https://mlsanity.com/paper/2307.01658/) (2023)
- [NetMind: Adaptive RAN Baseband Function Placement by GCN Encoding and Maze-solving DRL](https://mlsanity.com/paper/2401.06722/) (2024)

## Built on by (most cited first, 2 of 2)

- [Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations](https://mlsanity.com/paper/2503.23934/) (2025)
- [A Multi-Year Urban Streetlight Imagery Dataset for Visual Monitoring and Spatio-Temporal Drift Detection](https://mlsanity.com/paper/2512.12205/) (2025)

## Abstract (arXiv)

Seamless integration of artificial intelligence (AI) and machine learning (ML) techniques with wireless systems is a crucial step for 6G AInization. However, such integration faces challenges in terms of model functionality and lifecycle management. ML operations (MLOps) offer a systematic approach to tackle these challenges. Existing approaches toward implementing MLOps in a centralized platform often overlook the challenges posed by diverse learning paradigms and network heterogeneity. This article provides a new approach to MLOps targeting the intricacies of future wireless networks. Considering unique aspects of the future radio access network (RAN), we formulate three operational pipelines, namely reinforcement learning operations (RLOps), federated learning operations (FedOps), and generative AI operations (GenOps). These pipelines form the foundation for seamlessly integrating various learning/inference capabilities into networks. We outline the specific challenges and proposed solutions for each operation, facilitating large-scale deployment of AI-Native 6G networks.