Enhancing Network Automation with Generative AI

Generative AI has many new capabilities, but few engineers have integrated those features into existing network automation solutions. This course will teach you how to do exactly that using a realistic use-case.

This course cuts through today's hype surrounding generative AI by tying the output from the large language models (LLMs) into a complex network automation workflow. In this course, Enhancing Network Automation with Generative AI, you’ll learn to make intelligent requests of your generative AI model, then receive precise and accurate answers. First, you'll discover how to ask generative AI to convert network device configurations across vendors. Next, you’ll explore the power of post-AI/pre-deployment validation by combining off-line tools like pytest and batfish to catch obvious mistakes made by generative AI before deployment. Finally, you'll learn how to programmatically deploy network topologies and subsequently test them using an innovative combination of Python, GNS3, scrapli, textFSM, and asyncio. When you’re finished with this course, you’ll have the skills and knowledge of automation-oriented generative AI needed to solve specific business problems relating to network configuration, troubleshooting, deployment, and validation.

• Course Overview 1min
• Using GPT to Convert Network Configurations 33mins
• Optimizing AI with Fine-tuning and Embeddings 29mins
• Reviewing Network Configurations with Batfish and Pytest 39mins
• Building On-demand Test Topologies with GNS3 27mins
• Parsing Structured Data from CLI Outputs with TextFSM 18mins
• Validating Test Topologies with Scrapli and Asyncio 31mins