AI for Telecom: Network Optimization and Security in 5G/Edge Systems
1h 27mIntermediate2025-06-23
Authors
Taha Sajid
Course details
Unlock the power of AI in telecom with this hands-on course designed for both technical and non-technical audiences. Learn how AI optimizes networks, enhances security, and drives automation in 5G, ORAN, IoT, and edge computing environments. Gain practical skills to design and deploy AI-driven solutions using tools like Python, TensorFlow, and MATLAB. Discover how to evaluate AI’s impact on business performance, scalability, and compliance, empowering you to turn insights into action and future-proof telecom operations.
Learning objectives
Describe AI fundamentals and telecom integration.
Grasp core AI concepts and explore their role in transforming telecom networks through optimization, automation, and predictive analytics.
Analyze industry use cases and emerging trends.
Evaluate AI-driven use cases in 5G, ORAN, IoT, and edge computing, focusing on network performance, security, and sustainability.
Design AI-driven architectures and workflows.
Develop scalable AI frameworks to implement machine learning models, automate processes, and enable self-healing networks in telecom environments.
Apply Python, TensorFlow, MATLAB, and other AI frameworks to build, train, and deploy AI models tailored for telecom use cases.
Examine AI governance frameworks, including AI-RAN Alliance and ORAN security standards, to ensure compliance, scalability, and ethical implementation.
Evaluate business impact and ROI.
Measure the business value, cost savings, and operational improvements achieved through AI deployment in telecom networks.
Learning objectives
Describe AI fundamentals and telecom integration.
Grasp core AI concepts and explore their role in transforming telecom networks through optimization, automation, and predictive analytics.
Analyze industry use cases and emerging trends.
Evaluate AI-driven use cases in 5G, ORAN, IoT, and edge computing, focusing on network performance, security, and sustainability.
Design AI-driven architectures and workflows.
Develop scalable AI frameworks to implement machine learning models, automate processes, and enable self-healing networks in telecom environments.
Apply Python, TensorFlow, MATLAB, and other AI frameworks to build, train, and deploy AI models tailored for telecom use cases.
Examine AI governance frameworks, including AI-RAN Alliance and ORAN security standards, to ensure compliance, scalability, and ethical implementation.
Evaluate business impact and ROI.
Measure the business value, cost savings, and operational improvements achieved through AI deployment in telecom networks.
Skills covered
TelecommunicationsNetwork SecurityNetwork AdministrationCybersecurityNetwork and System AdministrationOne-Off
Concepts
0. Introduction
- 01 - Introduction to AI in telecom
- 02 - What you should know
1. AI Fundamentals and Telecom Integration
- 03 - AI introduction for telecom
- 04 - AI and network automation
- 05 - AI in predictive maintenance
- 06 - AI and data analytics
- 07 - Challenge - Flow-based anomaly detection
- 08 - Solution - Flow-based anomaly detection
2. AI Use Cases and Domains in Telecom
- 09 - AI domains in telecom
- 10 - AI for RAN
- 11 - AI for core networks
- 12 - AI for business support and operations
- 13 - AI for telecom security
- 14 - Exercise - AI model for anomaly detection
3. Designing and Deploying AI Architectures
- 15 - Lifecycle of an AI-driven architecture
- 16 - AI stack design
- 17 - Developing and testing an AI solution
- 18 - Operations phase in AI lifecycle
- 19 - AI security and governance
- 20 - Exercise - Self-healing network in 5G
4. Practical Implementation of AI
- 21 - Telecom AI maturity model
- 22 - AI architecture for telecom
- 23 - Building a RAG-based LLM
- 24 - Where to start with AI - Reference architecture
- 25 - Risks and ethical considerations for AI
- 26 - Exercise - Telecom LLM using RAG architecture for RCA
Conclusion
- 27 - Next steps