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 simulation3 labs3 quizzes

0/22 lessons

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AI/ML in Telecom Networks

0/8

Explore 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

Lab: Design a NWDAF analytics pipeline for congestion prediction

Build an end-to-end NWDAF pipeline — select data sources, choose ML models, configure consumers, and set alerting thresholds.

lab5 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

0/8

Master 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

0/6

Explore 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