Lilly Tech Systems
AI Network Configuration Management
Learn how machine learning and AI transform network configuration management — from detecting configuration drift and enforcing compliance to automating remediation and managing change workflows intelligently.
What You'll Learn
Master AI-driven configuration management for enterprise networks.
Drift Detection
Use ML models to detect configuration drift across thousands of devices automatically.
Compliance Checking
Enforce security policies and regulatory compliance using AI-powered config analysis.
Auto-Remediation
Build systems that automatically fix configuration issues and roll back failed changes.
Change Management
Integrate AI into change workflows for impact analysis and approval automation.
Course Lessons
Follow the lessons in order or jump to any topic you need.
1. Introduction
Overview of AI-driven configuration management: why traditional tools fall short and how AI fills the gap.
2. Config Drift Detection
Build ML models to detect unauthorized changes, configuration drift, and deviations from golden templates.
3. Compliance Checking
Use AI to validate configs against CIS benchmarks, NIST frameworks, PCI-DSS, and custom organizational policies.
4. Auto-Remediation
Design automated remediation pipelines with safety guardrails, rollback capabilities, and approval workflows.
5. Change Management
Integrate AI into ITSM workflows for change impact analysis, risk scoring, and intelligent scheduling.
6. Best Practices
Production deployment guidelines, governance frameworks, and organizational adoption strategies.
Prerequisites
What you need before starting this course.
- Understanding of network device configurations (routers, switches, firewalls)
- Basic knowledge of configuration management concepts
- Familiarity with Python and basic ML concepts
- Access to an AI/LLM service for hands-on exercises