In conjunction with IEEE/IFIP Network Operations and Management Symposium – NOMS 2026 // May 18–22 2026 // Rome // Italy
Defining the Future of Autonomous Networks We are witnessing a paradigm shift. Artificial Intelligence is no longer just an optimization tool; it is becoming the central nervous system of the 6G and IoT era. But as we hand over control to Generative AI, Large Language Models, and Deep Reinforcement Learning, we face a dual challenge: How do we build systems that are truly autonomous, and equally importantly, how do we engineer the lifecycle of the AI itself? AIMLOps 2026 is the premier forum to explore this frontier. We are looking for the boldest ideas—from self-driving network architectures to robust MLOps pipelines—that will define the next decade of network management.
Submission (6 pages including references): https://jems3.sbc.org.br/noms2026_aimlops_workshop
Please submit your cutting edge research that is potentially ongoing to spawn relevant discussions and get valuable feedback!
Modern network and service management has entered a new era, defined by the dual challenges and opportunities of Artificial Intelligence. This duality, highlighted by the NOMS 2026 theme “AI for Management and Management for AI,” forms the core of the AIMLOps workshop.
We invite original research, position papers, and experience reports. Topics of interest are organized around our two main pillars:
Pillar 1: AI for Management (Using AI to manage networks, services, and systems)
- Generative AI and Foundation Models: LLMs for intent-based management, automated troubleshooting, security management, and configuration generation.
- Deep Reinforcement Learning: DRL (incl. LSTMs) for dynamic resource orchestration, autonomous control, and QoE optimization.
- AI for Cybersecurity: Novel AI/ML methods for intrusion detection, malware analysis, anomaly detection, and autonomous security response.
- Predictive Analytics: DNNs (GNNs, Transformers) for traffic/failure prediction and root cause analysis.
- Applications: AI-powered Zero-Touch networks, intelligent 6G/IoT management, and autonomous operations.
- Explainability: XAI techniques for building trust in AI-driven management decisions.
Pillar 2: Management for AI (Managing the lifecycle of AI/ML models in operations)
- MLOps/AIOps/LLM-Ops: Frameworks, platforms, and best practices for managing AI in network operations.
- AI Lifecycle Management: Data engineering, model training, validation, and continuous deployment (CI/CD) pipelines.
- Security and Governance of AI: Securing MLOps pipelines, defense against adversarial attacks (e.g., data poisoning), ensuring model privacy, and managing model governance.
- AI Workload Management: Orchestration of distributed AI training and inference workloads across the cloud-edge continuum.
- Resource-Efficient AI: Techniques for federated learning, model compression, and green AI operations
Submitted papers must be original work, written in English, and not have been published or be under review elsewhere.
We solicit papers of up to 6 pages (including all figures, tables, and references). Submissions must be in PDF format and use the standard IEEE 2-column conference template.
Submissions will be managed via JEMS3: https://jems3.sbc.org.br/noms2026_aimlops_workshop
All accepted and presented papers will be published in the NOMS 2026 conference proceedings and submitted for inclusion in IEEE Xplore.
Important Dates (Tentative)
- Paper Submission Deadline:
January 19 2026,February 2, 2026 (extended),February 9, 2026 (firm) - Acceptance Notification:
March 2, 2026March 6, 2026 - Camera-Ready Deadline: March 16, 2026
- Workshop Date (T.B.C.!): May 18, 2026 (or May 22, 2026)
Workshop Organizers
- Marc-Oliver Pahl, IMT Atlantique, France, marc-oliver.pahl@imt-atlantique.fr
- Hanan Lutfiyya, University of Western Ontario, Canada, hlutfiyy@uwo.ca
- Stuart Clayman, UCL, UK, s.clayman@ucl.ac.uk