Ai In 5G Networks: Deployment Aspects, Risks And Telecom Llm

AI in Telecom - AI/ML adoption, LLM for 5G networks, on-device / cloud LLM and 5G AI challenges

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

Requirements

Basic understanding of telecom (5G networks)

No need for AI/ML knowledge

Description

AI adoption in 5G networks is already a reality!I give you 2.5 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.Doesn't mean who you are: CEO/CTO, a PhD student or 5G engineer - this course will give you full overview of AI/ML implementation aspects for 5G networks in your telecom company.By the end of this course, you will get an advantage by understanding:Basic AI/ML understanding related to Telecom networks including Generative AI, LLM, Federated LearningChallenges and potential solutions for Generative AI adoption in 5G mobile networksThe possibilities of Large Language Models (LLMs) for Telecom areas including on-device LLM and 5G MEC5G Infrastructure challenges and possible KPIs related to AI implementationEthical and privacy considerations related to AI in Telecom.In addition, you will navigate the current state of AI, LLM market landscape, LLM foundation models, Open-Source LLM for Telecom, possible use cases in 5G networks and additional materials in a form of attachments.You will have a possibility to check your knowledge after each paragraph.This course is designed for anyone curious about AI implementation in mobile networks.Let's rock telecom together!

Overview

Section 1: AI fundamentals: terminology and challenges

Lecture 1 Terminology: what is AI?

Lecture 2 Terminology: types of Machine Learning

Lecture 3 Terminology: Supervised/Unsupervised/Reinforcement Learning

Lecture 4 Terminology: Neural Networks

Lecture 5 Terminology: other types of AI/ML

Lecture 6 Terminology: Distributed Learning

Lecture 7 Terminology: Federated Learning

Lecture 8 Terminology: Generative AI

Lecture 9 Terminology: General AI

Lecture 10 Terminology: what is LLM?

Lecture 11 Terminology: multi-modal AI

Lecture 12 Terminology: AI-native

Lecture 13 Why AI is not = Human Capacity?

Lecture 14 AI and Work: middle class at risk?

Lecture 15 AI and Work: upskill, upskill, upskill(!)

Lecture 16 AI Ethical and Privacy Challenges

Section 2: AI adoption for Telecom: from challenges to solutions

Lecture 17 Gartner's Hype Cycle for AI and 5G

Lecture 18 AI for Telecom: history repeats itself

Lecture 19 AI for Telecom: areas of application

Lecture 20 Generative AI for Telecom: adoption and deployments

Lecture 21 Generative AI for Telecom: adoption challenges

Lecture 22 Why do you need to build an AI center of excellence?

Lecture 23 Generative AI for Telecom: take open approaches

Lecture 24 Generative AI for Telecom: areas of focus for adoption

Section 3: LLMs in Telecom: models, costs, infrastructure, KPIs, optimization.

Lecture 25 AI Index Report: current state of LLM

Lecture 26 Terminology: what is LLM and what it can do?

Lecture 27 LLM: Market Landscape overview

Lecture 28 LLM: how much does it cost to build, enhance or fine-tune foundation model?

Lecture 29 LLM: understanding Telecom language

Lecture 30 Telecom for LLM vs LLM for Telecom

Lecture 31 Telecom Infrastructure: on-device/MEC/Cloud LLMs

Lecture 32 Telecom Infrastructure: LLM edge and on-device challenges

Lecture 33 Collective Intelligence Concept

Lecture 34 Telecom LLM: possible network KPIs

Lecture 35 On-device LLM: Apple/Google/Samsung

Lecture 36 LLM: GTP-4, Gemini, Glaude vs Open Source models

Lecture 37 LLM: Proprietary models vs Open Source models

Lecture 38 On-device LLM: Inference limit

Lecture 39 On-device LLM: optimization example

Lecture 40 On-device LLM: semantic communication

Lecture 41 Telecom LLM: impact on mobile traffic and 3 new fundamental things for Telecom

CEO/CTO of telecom companies,5G RAN and Core engineers,PhD researchers and students