Advanced Network Automation with Cisco and Python

Manual network changes create operational risk: inconsistent configs, slow troubleshooting, and limited evidence when leaders need to prove what changed and why. IT Directors can assign this professional-level Cisco training to experienced network engineers or automation-focused IT Practitioners with roughly 3–5 years of exposure to network automation and CI/CD practices. The course is a significant enablement investment — about 32.7 hours per learner — and is best suited for teams standardizing Python-based automation across Cisco environments rather than onboarding engineers new to Python. Team Leads should plan change-management guardrails around credential handling, automated testing, configuration archiving, and pipeline adoption. CBT Nuggets Playlists can help sequence role-based rollout, while Team Reporting helps Training Managers track completion across the network engineering team.

IT teams complete this training to move network automation from isolated scripts toward repeatable, testable workflows that support production operations.

• Reduce change risk: Engineers practice automating configuration and change management with structured data, templates, filtering, and Nornir-based inventory abstraction.
• Improve troubleshooting speed: Teams build automations for Layer 2 issues, IP conflict detection, routing validation, reachability testing, and OSPF error detection.
• Strengthen operational evidence: Configuration and device-state archiving helps teams compare intended and actual network state after changes.
• Modernize automation architecture: Engineers work with NAPALM, pytest, pyATS, YANG, NETCONF, RESTCONF, gNMI, and NetDevOps pipeline concepts for scalable network operations.

Knowledge

• How Nornir supports inventory abstraction and concurrent task execution for network automation.
• How structured data, templates, and filtering improve repeatability across device groups.
• How credential management and automation security affect production network workflows.
• How NAPALM, pytest, and pyATS fit into network testing and validation.
• How YANG, NETCONF, RESTCONF, and gNMI support model-driven automation.
• How NetDevOps pipeline concepts apply to network configuration and validation.

Ability

• Build a lab environment for Cisco-focused Python network automation.
• Automate configuration changes, archiving, and device-state collection.
• Create scripts for Layer 2 troubleshooting, IP conflict detection, routing validation, reachability testing, and OSPF checks.
• Automate data center fabrics, DMVPN, VXLAN, and MPLS Layer 3 VPN scenarios.
• Use intent-based scripting and automated tests to validate network behavior.
• Implement a web front-end and pipeline-oriented workflows for automation delivery.