Network Automation with AI

Key Topics Covered Foundations of closed-loop automation, including the Monitor–Analyse–Decide–Act model and its role in improving service reliability, efficiency, and SLA performance across network domains Telemetry and real-time observability, covering data collection from multi-domain sources, streaming analytics, and integration with assurance systems Analytics and decision-making frameworks, including rule-based and AI-driven policy engines for SLA monitoring, predictive analysis, and automated decision triggers Automated orchestration and remediation actions, enabling scaling, rerouting, reconfiguration, and fault recovery across RAN, Core, Transport, and Cloud End-to-end service assurance integration, including KPI monitoring, SLA enforcement, incident automation, and alignment with operational processes Learning Outcomes Apply closed‑loop automation frameworks (monitor → analyse → decide → act) Identify relevant telemetry/KPI sources for real‑time decisions Explain interplay between analytics engines and orchestration layers Understand assisted vs full closed‑loop implementation models." Target Audience Network automation engineers AI/analytics engineers supporting network assurance OSS platform architects Operations teams responsible for service assurance and performance