All Courses
Level 18
Telecom AI/ML & Network Automation
AI/ML in telecom networks (NWDAF, ML lifecycle, RAN/core/CEM optimization), network automation & ZSM (closed-loop, intent-based, ONAP, IaC, API-driven), and digital twins & predictive operations (predictive maintenance, capacity forecasting, AIOps).
11 interactive5 simulation2 labs3 quizzes
0%
0/21 lessons
AI/ML in Telecom Networks
0/7Explore AI/ML opportunities across the telecom stack -- NWDAF architecture and services, ML model lifecycle management, and AI-driven optimization for RAN, core network, and customer experience.
AI/ML opportunities in telecom: RAN, core, transport, OSS/BSS
Survey of AI/ML use cases spanning RAN optimization, core network intelligence, transport automation, and OSS/BSS analytics.
interactive3 min
NWDAF: architecture, services, and data sources
The 3GPP Network Data Analytics Function -- its architecture, analytics services, and integration with 5G core network functions.
interactive3 min
ML model lifecycle in telecom: data -> training -> deployment -> monitoring
End-to-end ML model lifecycle management for telecom including data collection, training, validation, deployment, and continuous monitoring.
interactive3 min
AI/ML for RAN optimization: anomaly detection, traffic prediction, energy saving
Applying AI/ML to optimize RAN performance through anomaly detection, traffic forecasting, and intelligent energy savings.
simulation3 min
AI/ML for core network: slice SLA prediction, NF scaling, threat detection
AI-driven intelligence in the 5G core for predictive slice assurance, dynamic NF scaling, and security threat detection.
interactive3 min
AI/ML for customer experience: QoE prediction, churn, capacity planning
Leveraging AI/ML for customer experience management including QoE prediction, churn analysis, and demand-driven capacity planning.
interactive3 min
Quiz: AI/ML in telecom
Test your understanding of AI/ML applications across telecom network domains and NWDAF architecture.
quiz3 min
Network Automation & ZSM
0/8Master zero-touch service management, intent-based networking, closed-loop automation, orchestration platforms, Infrastructure as Code, and API-driven network operations.
ETSI ZSM framework: zero-touch service management
The ETSI ZSM reference architecture for fully automated, zero-touch end-to-end service management across network domains.
interactive3 min
Intent-based networking: intent -> policy -> configuration -> assurance
How intent-based networking translates business objectives into automated network configuration and continuous assurance.
interactive3 min
Closed-loop automation: monitor -> analyze -> decide -> act (MADA)
The MADA closed-loop automation cycle enabling autonomous network operations through continuous monitoring and adaptive actions.
interactive3 min
Network orchestration: ONAP, OSM, Cloudify
Comparing major network orchestration platforms -- ONAP, Open Source MANO, and Cloudify -- for telecom service orchestration.
simulation3 min
Infrastructure as Code: Ansible, Terraform for network automation
Applying IaC principles with Ansible and Terraform to automate telecom network provisioning and configuration management.
interactive3 min
API-driven operations: RESTCONF, NETCONF, gNMI
Programmable network operations using RESTCONF, NETCONF, and gNMI for model-driven configuration and telemetry streaming.
simulation3 min
Real-world: Automated self-healing -- detect -> root cause -> remediate
Walk through a real-world self-healing automation scenario from fault detection through root cause analysis to automated remediation.
lab5 min
Quiz: Network automation
Test your knowledge of ZSM, intent-based networking, closed-loop automation, and orchestration platforms.
quiz3 min
Digital Twins & Predictive Operations
0/6Explore network digital twins, predictive maintenance with ML, capacity forecasting using time-series models, AIOps for intelligent operations, and hands-on anomaly detection pipeline design.
Network digital twins: concept, architecture, data requirements
The concept and architecture of network digital twins including data ingestion, modeling, and simulation requirements.
interactive3 min
Predictive maintenance: using ML to predict hardware failures
Applying ML models to predict network hardware failures before they occur using telemetry and environmental data.
simulation3 min
Capacity forecasting: traffic prediction with time-series ML
Time-series ML techniques for forecasting network traffic demand and proactive capacity planning.
simulation3 min
AIOps: AI-driven operations, event correlation, intelligent alerting
AIOps platforms that use AI for event correlation, noise reduction, root cause analysis, and intelligent alerting.
interactive3 min
Lab: Design an AI/ML pipeline for anomaly detection in RAN KPIs
Hands-on lab designing an end-to-end AI/ML pipeline for detecting anomalies in RAN key performance indicators.
lab5 min
Quiz: Digital twins & predictive operations -- Level Gate
Comprehensive assessment covering digital twins, predictive maintenance, capacity forecasting, and AIOps concepts.
quiz5 min