Course Modules

Module 1: Intelligent Network Operations and Digital Transformation

• Evolution of telecom operations
• Industry challenges driving automation
• Cloud-native telecom infrastructure
• Intelligent network management
• AI-driven operational transformation
• Emerging telecom operating models
• Case Study & Practical Application: Assessing the automation maturity of a telecom operator and identifying opportunities for intelligent transformation.

Module 2: Software-Defined Networking (SDN), NFV and Cloud-Native Networks

• SDN architecture
• Network Functions Virtualization
• Cloud-native telecom platforms
• Containerization and microservices
• Kubernetes for telecom applications
• Automation opportunities
• Case Study & Practical Application: Designing an automated cloud-native telecom architecture integrating SDN and NFV.

Module 3: Artificial Intelligence for Intelligent Network Operations

• AI in telecom operations
• Machine learning for operational intelligence
• Network analytics
• Intelligent event correlation
• Root cause analysis
• Predictive operational intelligence
• Case Study & Practical Application: Applying AI to optimize operational workflows within a telecom Network Operations Centre.

Module 4: Network Automation Platforms and Orchestration

• Intelligent orchestration
• Service orchestration
• Automated provisioning
• Configuration management
• Intent-based networking
• Closed-loop automation
• Case Study & Practical Application: Developing an automated telecom service orchestration workflow to reduce deployment time and operational complexity.

Module 5: Digital Twins for Telecommunications

• Digital twin concepts
• Telecom infrastructure modelling
• Network simulation
• Predictive network behaviour
• Capacity planning
• Infrastructure optimization
• Case Study & Practical Application: Building a digital twin model to simulate and optimize the performance of a metropolitan telecommunications network.

Module 6: Self-Healing and Self-Optimizing Networks (SON)

• Self-healing network principles
• Self-configuration
• Self-optimization
• Automated fault recovery
• AI-driven network adaptation
• Autonomous decision-making
• Case Study & Practical Application: Developing a self-healing strategy for reducing network outages and improving service continuity.

Module 7: Predictive Operations and Intelligent Service Assurance

• Predictive maintenance
• AI-enabled service assurance
• QoS and QoE monitoring
• Intelligent alarm management
• Performance optimization
• Customer experience analytics
• Case Study & Practical Application: Implementing predictive service assurance to proactively identify and resolve service quality issues.

Module 8: Edge Computing, Edge AI and Intelligent Distributed Networks

• Edge computing architectures
• Multi-access Edge Computing (MEC)
• Edge AI applications
• Distributed intelligence
• Low-latency telecom services
• AI-enabled IoT ecosystems
• Case Study & Practical Application: Designing an edge AI solution to support low-latency services in a 5G telecommunications environment.

Module 9: Cybersecurity Automation and Intelligent Risk Management

• AI-powered cybersecurity
• Automated threat detection
• Behavioural analytics
• Security orchestration and automation
• Zero Trust concepts
• AI governance and compliance
• Case Study & Practical Application: Developing an automated cybersecurity monitoring and incident response framework for telecom infrastructure.

Module 10: Future Autonomous Networks and Telecom Innovation

• 5G Advanced
• 6G intelligent networks
• Autonomous network maturity
• Open RAN automation
• Generative AI in telecom operations
• Future intelligent network ecosystems
• Enterprise implementation strategies
• Case Study & Practical Application: Developing a comprehensive roadmap for implementing autonomous telecom networks incorporating AI, automation, digital twins, cloud-native technologies, and future telecommunications innovations.