Lilly Tech Systems
AI-Driven Network Automation
Move beyond script-based automation to intelligent, self-driving networks. Learn how AI enables closed-loop automation, self-healing infrastructure, intent-based policy engines, and end-to-end orchestration across multi-vendor environments.
What You'll Learn
Build the skills to design and implement AI-powered network automation systems.
Closed-Loop Automation
Design systems that observe, analyze, decide, and act autonomously to maintain desired network state.
Self-Healing Networks
Build networks that detect failures and automatically remediate issues without human intervention.
Policy Engines
Implement intent-based networking with AI-powered policy engines that translate business intent into network configuration.
Orchestration
Coordinate automation across multi-vendor, multi-domain environments using AI-driven orchestration platforms.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
The evolution from manual to AI-driven automation. Understand the automation maturity model and where AI fits in.
2. Closed-Loop Automation
Design observe-analyze-act loops: telemetry collection, AI-based analysis, automated decision making, and safe execution.
3. Self-Healing Networks
Implement automated fault detection, root cause analysis, remediation playbooks, and rollback mechanisms.
4. Policy Engines
Intent-based networking: translating business policies to network configs using AI, verification, and compliance checking.
5. Orchestration
Multi-domain orchestration with NSO, Terraform, and AI-driven workflows across campus, WAN, data center, and cloud.
6. Best Practices
Safety guardrails, change management, testing automation, human-in-the-loop design, and scaling AI automation.
Prerequisites
- Experience with network automation (Ansible, Python, or similar)
- Understanding of network protocols and architectures
- Familiarity with AI/ML concepts (from earlier courses)
- Basic knowledge of APIs and data formats (JSON, YAML)