Duation: 1 Day
Description
This one-day course explores how artificial intelligence and machine learning can enhance network automation in 5G and cloud-native telecommunications environments. It explains the progression from configuration automation to orchestration, assurance and autonomy, and distinguishes rule-based automation from AI-assisted approaches.
Participants examine practical applications across RAN, core, transport and cloud domains, including load prediction, anomaly detection, path optimisation and automated scaling. The course introduces the machine learning concepts, data requirements and operational constraints that underpin these applications.
The course also explores how AI supports closed-loop and intent-based automation, including interaction with orchestration and assurance systems. Participants consider telemetry, model deployment and integration with existing operational systems, alongside the controls needed to ensure reliable and safe automated actions.
Course Contents
- Foundations of Network Automation in 5G/Cloud Environments
- AI & ML Concepts for Telecom Networks
- AI-Enhanced Automation Use Cases Across Network Domains
- Integrating AI into Closed Loop and Intent-Based Automation
- Data, Telemetry & Model Deployment Architecture
Who the Course Is For
This course is intended for network engineers, technical architects, network operations and assurance specialists, and professionals involved in network automation, orchestration or operational support systems. It is particularly relevant to those working across RAN, core, transport and cloud domains who need to understand how AI and machine learning can improve network operations and support the progression towards autonomous networks.
Learning Objectives
By the end of the course, participants will be able to:
- Explain the evolution from automation to orchestration, assurance and autonomous networks.
- Differentiate rule-based and AI-assisted automation and their appropriate use cases.
- Describe supervised, unsupervised and reinforcement learning in telecoms.
- Explain the machine learning lifecycle from data preparation to deployment and retraining.
- Identify AI-driven automation opportunities across RAN, core, transport, cloud and enterprise networks.
- Explain predictive and reactive closed-loop automation.
- Describe how intent is translated into policies and automated network actions.
- Identify the telemetry, metrics, logs and traces required for network AI.
- Compare edge and central cloud AI deployment models.
- Explain key considerations for AI governance, accuracy and OSS integration.
