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Quest Training

AIOps & Self-Organizing Networks (SON) Training Course

Shows telecom teams how AIOps and self-organizing networks use machine learning and automation to improve fault management, performance and service quality.

TADT · Telecommunications Automation & Digital TransformationAll Levels#2588ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Telecommunications & Satellite TechnologyCategories:Telecommunications Automation & Digital Transformation

Course Overview

This advanced programme provides a practical understanding of AIOps and Self-Organizing Networks (SON) and their application in modern telecommunications operations. It focuses on using artificial intelligence, machine learning, automation, and network intelligence to improve operational efficiency, service quality, network performance, and fault management. The course examines how AIOps can process large volumes of network and operational data to detect anomalies, identify patterns, predict incidents, support root-cause analysis, and automate operational responses. It also explores how SON capabilities can optimise network configuration, coverage, capacity, mobility, and performance with reduced manual intervention. Participants will examine the relationship between network telemetry, data platforms, AI and machine learning models, operational systems, SON functions, orchestration, and closed-loop automation. Particular attention is given to practical implementation considerations, including data quality, model performance, automation policies, operational governance, and human oversight. Through practical scenarios, participants will analyse network performance problems, develop AI-driven operational workflows, design SON use cases, evaluate automation opportunities, and build structured approaches for deploying intelligent and self-optimising network operations.

Learning Objectives

  • Analyse the principles and architecture of AIOps in telecommunications.
  • Understand the concepts, functions, and operating models of Self-Organizing Networks.
  • Evaluate the role of AI and machine learning in network operations and optimisation.
  • Analyse network telemetry, event, alarm, performance, and configuration data.
  • Apply anomaly detection and predictive approaches to telecom operational challenges.
  • Develop structured approaches to automated root-cause analysis.
  • Evaluate SON use cases for self-configuration, self-optimisation, and self-healing.
  • Design closed-loop operational workflows combining AIOps, SON, and network automation.
  • Assess data quality, model performance, automation risks, and operational dependencies.
  • Identify opportunities for predictive maintenance and proactive network management.
  • Develop governance and human-oversight mechanisms for AI-driven network operations.
  • Build practical implementation strategies for AIOps and SON adoption.

Who Should Attend

This programme is designed for Telecom Engineers, Network Engineers, Radio Network Engineers, Network Operations Professionals, AIOps Engineers, SON Specialists, Network Automation Engineers, and Telecom Architects. It is also suitable for NOC Managers, Service Assurance Professionals, Performance Engineers, Data and AI Professionals working in telecommunications, IT Operations Professionals, and technical managers involved in network transformation and intelligent operations. The programme is particularly relevant to telecom operators and service providers implementing AI-driven operations, automated network optimisation, predictive assurance, closed-loop automation, or next-generation network management.

Learning Outcomes

  • Explain the architecture and operational role of AIOps in telecom environments.
  • Describe SON principles and distinguish self-configuration, self-optimisation, and self-healing functions.
  • Analyse network telemetry, alarms, events, and performance data for operational insights.
  • Identify anomalies and performance patterns using structured analytical approaches.
  • Develop practical approaches to predictive incident and fault management.
  • Apply root-cause analysis techniques supported by AI-driven operational intelligence.
  • Evaluate SON use cases across coverage, capacity, mobility, and performance optimisation.
  • Design closed-loop workflows connecting detection, analysis, decision-making, and automated action.
  • Assess data, integration, model, and automation dependencies affecting AIOps and SON.
  • Define appropriate KPIs for intelligent network operations and optimisation.
  • Establish governance, validation, and human-oversight controls for AI-driven decisions.
  • Develop an implementation roadmap for AIOps and SON within a telecom operating environment.

Course Outline

Day 1

AIOps Fundamentals for Telecommunications

  • Evolution from traditional network operations to intelligent operations
  • AIOps principles and operating models
  • AI and machine learning in telecom operations
  • Network telemetry and operational data sources
  • Alarms, events, logs, performance, and configuration data
  • Data aggregation and operational intelligence
  • Anomaly detection and pattern recognition
  • Predictive operations and incident management
  • AIOps integration with OSS/BSS and network management platforms
  • Practical Application: Analyse a telecom operations dataset and identify anomalies, performance patterns, and potential incidents
Day 2

Self-Organizing Networks & Network Intelligence

  • Principles and architecture of Self-Organizing Networks
  • Self-configuration capabilities
  • Self-optimisation capabilities
  • Self-healing capabilities
  • SON use cases in radio and mobile networks
  • Coverage and capacity optimisation
  • Mobility and handover optimisation
  • Interference and parameter optimisation
  • SON interaction with network management and automation platforms
  • Practical Application: Develop a SON use case for improving network coverage, capacity, or performance
Day 3

AI-Driven Network Analytics & Predictive Operations

  • Network performance analytics
  • Feature engineering and operational data preparation
  • Anomaly and event correlation
  • Predictive fault and incident detection
  • Root-cause analysis
  • Failure prediction and proactive maintenance
  • Network performance forecasting
  • AI-assisted alarm management
  • Model accuracy, validation, and operational reliability
  • Practical Application: Develop an AI-driven workflow for detecting, analysing, and predicting a network performance issue
Day 4

Closed-Loop Automation, SON & Intelligent Operations

  • Closed-loop network operations
  • Detection, analysis, decision, and action cycles
  • Policy-driven network optimisation
  • Automated remediation
  • AIOps integration with network automation and orchestration
  • SON coordination across network domains
  • Event-driven automation
  • Human-in-the-loop and human-on-the-loop operating models
  • Security, governance, and automation risks
  • Practical Application: Design a closed-loop workflow connecting AIOps analytics, SON decisions, network automation, and automated remediation
Day 5

AIOps & SON Strategy, Governance & Implementation

  • AIOps and SON operating models
  • Intelligent network operations architecture
  • Data governance and data quality
  • AI model lifecycle and monitoring
  • Automation policies and operational controls
  • Measuring AIOps and SON performance
  • Operational maturity assessment
  • Scaling intelligent operations across network domains
  • Implementation challenges and transformation priorities
  • Final Workshop: Develop an integrated AIOps & SON implementation roadmap covering data sources, AI analytics, anomaly detection, predictive operations, SON use cases, closed-loop automation, KPIs, governance, human oversight, and deployment priorities

Upcoming Dates

Selected date

Paris · 11 – 15 Jan 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
LisbonScheduled
2 – 6 Nov 2026€6,000—
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LondonScheduled
9 – 13 Nov 2026€5,800—
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MadridScheduled
16 – 20 Nov 2026€6,000—
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MakkahScheduled
23 – 27 Nov 2026€4,800€4,320until 2 Nov
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ManamaScheduled
30 Nov – 4 Dec 2026€5,300€4,770until 9 Nov
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MarbellaScheduled
7 – 11 Dec 2026€6,000€5,400until 16 Nov
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MarrakeshScheduled
7 – 11 Dec 2026€4,700€4,230until 16 Nov
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MilanScheduled
14 – 18 Dec 2026€7,000€6,300until 23 Nov
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MunichScheduled
21 – 25 Dec 2026€6,000€5,400until 30 Nov
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MuscatScheduled
28 Dec 2026 – 1 Jan 2027€6,300€5,670until 7 Dec
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OnlineScheduled
3 – 7 Jan 2027€2,700€2,430until 14 Dec
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ParisScheduled
11 – 15 Jan 2027€6,700€6,030until 21 Dec
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RabatScheduled
18 – 22 Jan 2027€4,700€4,230until 28 Dec
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RiyadhScheduled
18 – 22 Jan 2027€4,800€4,320until 28 Dec
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RomeScheduled
25 – 29 Jan 2027€7,000€6,300until 4 Jan
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Sharm El-SheikhScheduled
1 – 5 Feb 2027€5,300€4,770until 11 Jan
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TunisiaScheduled
8 – 12 Feb 2027€4,300€3,870until 18 Jan
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ViennaScheduled
15 – 19 Feb 2027€6,400€5,760until 25 Jan
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AmsterdamScheduled
22 – 26 Feb 2027€6,000€5,400until 1 Feb
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BakuScheduled
1 – 5 Mar 2027€5,300€4,770until 8 Feb
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BerlinScheduled
8 – 12 Mar 2027€6,300€5,670until 15 Feb
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BarcelonaScheduled
8 – 12 Mar 2027€6,000€5,400until 15 Feb
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CairoScheduled
15 – 19 Mar 2027€3,800€3,420until 22 Feb
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CasablancaScheduled
22 – 26 Mar 2027€4,700€4,230until 1 Mar
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DammamScheduled
29 Mar – 2 Apr 2027€4,800€4,320until 8 Mar
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DohaScheduled
5 – 9 Apr 2027€4,700€4,230until 15 Mar
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DubaiScheduled
11 – 15 Apr 2027€4,700€4,230until 22 Mar
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GenevaScheduled
19 – 23 Apr 2027€7,300€6,570until 29 Mar
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Kuala LumpurScheduled
26 – 30 Apr 2027€4,700€4,230until 5 Apr
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IstanbulScheduled
26 – 30 Apr 2027€5,300€4,770until 5 Apr
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Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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