As networks become increasingly complex, traditional reactive maintenance approaches are proving inadequate for ensuring optimal performance and reliability. This session explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in shifting from reactive to proactive network management, focusing on predictive intelligence, smart troubleshooting, and event impact analysis to build self-healing systems.

We will delve into the evolution from conventional network maintenance to AI-powered prediction and automation, highlighting use cases where predictive maintenance has successfully reduced failures before they occur. The session will also examine how AI/ML tools, such as Network Engineering Co-pilots, enable dynamic event impact prediction for Radio Access Networks (RAN). Real-world outcomes of ML-assisted troubleshooting using System Behavior Analysis (SBA) will be discussed, showcasing benefits like faster root cause identification, improved decision-making, and reduced downtime.

Target Audience

• Network Engineers & Architects: Professionals involved in designing and maintaining network infrastructures seeking to integrate AI for enhanced resilience.​    
• AI/ML Practitioners: Individuals interested in applying machine learning techniques to network operations and management.​
• Telecommunications Specialists: Experts focusing on Radio Access Networks and looking to leverage AI for improved performance and reliability.​
• IT Operations Managers: Leaders aiming to transition their organizations from reactive to proactive network maintenance strategies.​

By attending this session, participants will gain insights into the practical applications of AI and ML in modern network management, equipping them with knowledge to enhance network resilience and operational efficiency.