What you'll learn:
- Understand AI/ML basics for Mobile Networks
- Identify the aspects of AI deployment in Telecom
- Examine the challenges and solutions for Generative AI (LLMs) adoption in Telecom
- Gain in-depth knowledge about Telecom LLMs and such aspects as on-device LLMs / proprietary and open-source LLM
AI adoption in 5G networks is already a reality!
This is not another surface-level “AI for telecom” overview.
I give you 5.5 hours of well-structured video presentations in simple words when I will help you to gain a competitive knowledge to be ahead of everybody in AI adoption.
The only course where 5G engineers, CTOs, and telecom researchers get the complete picture — standards, deployment realities, LLM economics, and the roadmap to 6G AI-native architecture. No hype. No marketing.
By the end of this course, you'll understand:
Basic AI/ML concepts related to telecom networks, including Gen AI, Large Language Models (LLMs), and Federated Learning.
The potential of LLMs in telecom areas, such as on-demand LLM and 5G Multi-Edge Computing (MEC).
The truth about on‑device LLM inference, semantic communication, and the coming 10x uplink explosion driven by AI+AR devices (or not?).
5G infrastructure challenges and KPIs related to AI features and implementation.
How AI‑driven beam management, CSI feedback, and UEpositioning are being standardized in 3GPP - and what you already can implement right now.
Why AI‑native air interfaces and deep neural receivers will soon replace conventional RF blocks - and how to prepare?
But we also confront the uncomfortable truths:
Why most AI “solutions” will never reach production - and how to spot them.
The hidden TCO of AI‑infrastructure, model generalization gaps, and control‑layer risks.
How AI traffic will break current QoS models and force to re‑engineer our telecom networks.
The ethical, privacy, and workforce upheavals that come with true AI adoption.
You will have a possibility to check your knowledge after each paragraph.
Let's rock telecom together!