Course Outline
Introduction to AI in Telecommunications
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Telecom transformation and the role of AI.
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The telecom value chain: network, operations, service, customer, and business layers.
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AI, machine learning, deep learning, generative AI, and intelligent automation.
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Common telecom AI use cases and their expected business value.
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Distinguishing prediction, recommendation, optimization, automation, and autonomy.
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Exercise: Prioritizing AI opportunities for a communications service provider.
Telecom Data and AI Foundations
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Telecom data sources:
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Network performance counters and KPIs.
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Alarms, events, logs, and traces.
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Call detail records and usage data.
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QoS, QoE, and service-assurance data.
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Customer, billing, ticket, and interaction data.
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Location, device, and IoT telemetry.
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Structured, semi-structured, streaming, and time-series data.
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Supervised, unsupervised, and reinforcement-learning concepts.
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Classification, regression, clustering, anomaly detection, and forecasting.
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Data quality, missing values, class imbalance, and data leakage.
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Exercise: Exploring and preparing a representative telecom dataset.
Building the Telecom AI Data Pipeline
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Translating an operational problem into an AI problem.
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Defining labels, features, prediction windows, and success measures.
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Batch and streaming data pipelines.
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Feature engineering for alarms, KPIs, traffic, and customer behavior.
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Training, validation, and test-data design.
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Accuracy, precision, recall, F1 score, ROC-AUC, MAE, RMSE, and business KPIs.
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Avoiding misleading model performance.
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Exercise: Designing a data pipeline for a selected telecom use case.
Traffic Forecasting, Capacity Planning, and QoS/QoE
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Traffic-pattern analysis and demand forecasting.
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Time-series features, seasonality, trends, and anomalies.
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Predicting congestion and capacity requirements.
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AI-assisted resource allocation and traffic management.
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Relating technical network KPIs to service and customer experience.
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ML-based QoS/QoE assurance concepts.
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Hands-on lab: Building and evaluating a traffic-forecasting model.
Predictive Maintenance and Intelligent Service Assurance
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Moving from reactive to predictive operations.
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Fault prediction and early-warning models.
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Network alarm correlation, suppression, and prioritization.
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Anomaly detection in performance counters and telemetry.
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Supporting root-cause analysis with AI.
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Estimating risk, impact, and remaining useful life.
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Human-in-the-loop escalation and decision support.
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Hands-on lab: Detecting abnormal network behavior and ranking incidents.
AI for Network Optimization, 5G, Edge, and IoT
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AI applications across RAN, core, transport, and telco cloud.
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Coverage, capacity, mobility, and energy-optimization use cases.
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5G network slicing and service-level optimization.
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Edge inference and low-latency decision making.
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AI/ML concepts in intelligent and open RAN environments.
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IoT device intelligence, fault detection, and lifecycle management.
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Network digital twins and simulation-assisted optimization.
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Exercise: Selecting an AI architecture for a 5G or IoT scenario.
AI for Telecom Security and Fraud Management
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Telecom threat and fraud landscape.
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Detecting unusual subscriber, device, traffic, and access behavior.
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Fraud-risk scoring and imbalanced datasets.
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AI-assisted detection of network attacks and service abuse.
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False-positive management and explainable alerts.
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Adversarial threats, model poisoning, and attacks against AI systems.
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Exercise: Designing a fraud or security anomaly-detection workflow.
AI for Customer Experience and Commercial Operations
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Churn prediction and retention prioritization.
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Customer segmentation and next-best-action models.
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Personalized offers and service recommendations.
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Sentiment, intent, and topic analysis from customer interactions.
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AI assistants, chatbots, and agent-assist use cases.
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Contact-center summarization and knowledge retrieval.
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Measuring customer and business outcomes.
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Hands-on lab: Building a churn-risk model or customer-interaction classifier.
Deploying and Operating AI in a Telecom Environment
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Telecom ML pipelines and model lifecycle management.
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Deployment at cloud, edge, and on-premises locations.
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MLOps: versioning, testing, deployment, monitoring, and rollback.
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Model performance, drift, data drift, and retraining.
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Integration with OSS/BSS, NOC, ticketing, and orchestration platforms.
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Scaling from proof of concept to production.
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Build-versus-buy and vendor-neutral architecture considerations.
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Exercise: Creating a production deployment and monitoring plan.
Responsible AI, Governance, and Implementation Roadmap
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Privacy, security, transparency, explainability, and accountability.
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Responsible use of subscriber, location, and interaction data.
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Bias and fairness in customer-facing models.
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Risk classification and human oversight.
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Model and data ownership.
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Measuring technical, operational, customer, and financial value.
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Capstone: Presenting an AI solution for a telecom business or network problem.
Format of the Course
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Interactive lecture and discussion.
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Telecom case studies and scenario-based exercises.
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Hands-on exercises using Python, guided notebooks, and representative telecom datasets.
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A final use-case design or proof-of-concept project.
Course Customization Options
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The course can be customized for mobile, fixed, ISP, tower, satellite, or enterprise telecom environments.
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Labs can emphasize network optimization, service assurance, security/fraud, customer experience, 5G, or IoT.
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Client data can be used only when it has been appropriately anonymized, approved, and prepared for training.
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To request a customized training for this course, please contact us to arrange.
