AI & Generative AIAIMachine LearningTelecom AnalyticsAnomaly DetectionForecasting

AI for Telecom Engineers

Machine learning applied to network data — KPI forecasting, anomaly detection, root-cause classification, traffic prediction and energy saving — taught by engineers who understand both the network and the models.

Live OnlineClassroomOn-Site Corporate
Duration
3 days
Level
Intermediate
Delivery
Live Online · Classroom · On-Site Corporate
Location
Live Online · Dubai · GCC
Certificate
Certificate of Completion
Format
Instructor-Led

Course overview

This program is for telecom engineers, not data scientists. It teaches the ML concepts that matter for network problems and applies them hands-on to realistic network datasets, with clear guidance on what works in production.

Who should attend

  • RAN, core and performance engineers
  • Network analytics and planning teams
  • Engineering managers evaluating AI initiatives

Prerequisites

  • Basic Python (or attend Python for Telecom first)

Learning objectives

  1. 1Explain ML fundamentals in a network context
  2. 2Build forecasting and anomaly detection models on KPI data
  3. 3Classify network faults from features
  4. 4Evaluate models and avoid common pitfalls
  5. 5Identify high-value AI use cases in RAN, core and operations

Detailed curriculum

01AI in Telecom
  • Use-case landscape
  • Data sources: counters, traces, tickets
  • ML fundamentals
02Applied ML
  • Time-series forecasting
  • Anomaly detection
  • Classification for fault diagnosis
  • Feature engineering from KPIs
03RAN & Core Applications
  • Energy saving
  • Traffic steering and load balancing
  • Predictive maintenance
  • QoE modelling
04Production Considerations
  • MLOps basics
  • Model monitoring
  • Explainability
  • Governance

Day-by-day agenda

Day 1

  • AI in Telecom
  • Applied ML

Lecture, whiteboard analysis, exercises and lab time.

Day 2

  • RAN & Core Applications
  • Production Considerations

Lecture, whiteboard analysis, exercises and lab time.

Hands-on exercises & labs

  • KPI forecasting model
  • Cell-level anomaly detection
  • Fault classification from network features

Skills gained

Applied ML on network dataUse-case evaluationModel assessment

Instructor

Instructor profile — to be added

Name, engineering background and delivery experience of the assigned instructor will be published here. No credentials are shown until confirmed.

Training methodology

Instructor-led

Concepts explained by engineers with delivery experience, at whiteboard depth.

Trace & case driven

Real message flows, counters and scenarios connect theory to network behaviour.

Hands-on

Labs and design workshops matched to the program and delivery mode.

Available locations

Live online region-wide. Classroom and on-site delivery available in Dubai and across major GCC locations, arranged per cohort or corporate engagement.

Live OnlineDubaiAbu DhabiRiyadhJeddahDohaMuscatManamaKuwait City

Upcoming schedule

Live OnlineLive Online3 daysQ4 2026 · Dates to be announcedRegister Interest
ClassroomDoha3 daysQ1 2027 · Dates to be announcedRegister Interest

Cohort dates are published when confirmed. Register interest to be notified first, or request a corporate delivery on your own dates.

Frequently asked questions

Live online programs are delivered in real time by an instructor over video with shared labs and interactive Q&A. Classroom programs are delivered face to face in Dubai or other GCC locations, with the instructor and lab environment in the room. Both use the same curricula and instructors.

Programs are built on 3GPP, O-RAN and industry standards and are vendor-agnostic by default. Corporate deliveries can include vendor-specific parameter mapping and scenarios where required.

Participants who complete a program receive a Teleriu certificate of completion. Teleriu programs are professional technical training and are not affiliated with any certification body unless explicitly stated.

Related programs

Bring this program to your engineering team

Customised for your vendors, architecture and objectives — delivered on-site, in the classroom or live online.

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