Intelligent NetDevOps: Automating Network Engineering with AI (Video Course)

Description

4+ Hours of Video Instruction

Unlock the next evolution of network automation by learning how to combine NetDevOps practices with AI, machine learning, and large language models to build intelligent, proactive, and resilient network operations systems.

Take your network automation skills into the AI era with AI for Network Engineering: Building Intelligent NetDevOps. This advanced course introduces the next major shift in network operations: moving beyond brittle scripts and reactive troubleshooting toward intelligent systems that can reason, predict, validate, and act.

In this course, youll learn how to combine proven NetDevOps principles with the power of modern artificial intelligence, including machine learning, predictive analytics, and large language models. Youll explore how AI can enhance traditional network automation workflows by helping engineers detect anomalies, validate complex configuration changes, troubleshoot faster, query the network through ChatOps, and generate useful operational documentation.

Youll begin by reviewing the limits of traditional automation and why modern networks require a more adaptive approach. From there, youll learn practical architectural patterns for integrating AI into NetDevOps pipelines using familiar tools such as Ansible, Terraform, Git-based workflows, and network source-of-truth platforms alongside AI frameworks and libraries such as Scikit-learn, PyTorch, LangChain, and Hugging Face. Through real-world examples, youll see how intelligent automation can reduce operational overhead, improve network resiliency, and support proactive decision-making.

As the course progresses, youll examine four key use cases for AI-enhanced network operations: intelligent configuration validation, AI-assisted troubleshooting, ChatOps for network teams, and automated documentation with a RAG-based knowledge base. Youll also learn important best practices for building trustworthy AI-driven systems, including how to manage data quality, reduce model risk, address hallucinations, secure AI workflows, and determine when AI isand is notthe right tool for the job.

By the end of this course, youll understand how to design and build next-generation automation solutions that go beyond task execution. Youll be prepared to create network operations systems that can learn from data, reason through complex conditions, and help engineers manage increasingly complex infrastructure with greater confidence. Whether youre a network engineer, automation specialist, architect, or infrastructure professional, AI for Network Engineering: Building Intelligent NetDevOps will help you move from traditional automation to the future of intelligent, self-adapting networks.

All Jupyter notebooks used throughout this course are available in the companion GitHub repository at https://github.com/marcomobar/intelligent-netdevops, so you can run every demonstration in your own environment as you follow along.

Skill Level:

Intermediate to advanced

Learn How To:

• Apply AI and machine learning concepts to real-world network automation challenges
• Design intelligent NetDevOps pipelines that combine automation tools with AI frameworks
• Use AI to improve configuration validation, troubleshooting, optimization, and documentation
• Integrate tools such as Ansible, Terraform, GitLab, NetBox, Scikit-learn, PyTorch, LangChain, and Hugging Face
• Build AI-augmented workflows that reduce operational overhead and improve network resiliency
• Recognize the risks, limitations, and best practices of using AI in network operations

Course Requirement:

Learners should have a solid understanding of networking fundamentals and experience with at least one scripting language, such as Python. Familiarity with core NetDevOps concepts, including Infrastructure as Code, CI/CD workflows, Git-based operations, and network automation tools, is strongly recommended.

Who Should Take This Course:

This course is designed for practicing network engineers, network architects, DevOps engineers, Site Reliability Engineers, infrastructure automation developers, and technical leaders who want to evolve from traditional automation to intelligent, AI-driven network operations.

It is ideal for professionals who already understand the value of automation but are ready to move beyond static scripts, manual validation, and reactive troubleshooting. If you work with enterprise networks, service provider environments, cloud infrastructure, or large-scale automation systems, this course will help you understand how AI can be applied practically and responsibly to modern network operations.

Learners who complete this course will be better prepared to design smarter automation systems, evaluate emerging AI networking tools, and build resilient workflows that support the next generation of intelligent infrastructure.

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more.  Learn more about Pearson Video Training at  http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents

Lesson 1: Foundations of Intelligent NetDevOps Learning objectives

1.1 Why NetDevOps needs AI

1.2 Core principles of NetDevOps

1.3 AI essentials through a networking lens

1.4 From rules to learning

Lesson 2: Bridging NetDevOps and AI

2.1 When AI adds value

2.2 NLP and LLMs for network operations

2.3 The NetDevOps and AI tooling landscape

2.4 Architectural patterns for AI-NetDevOps

Lesson 3: Intelligent CICD and AI-Enhanced Troubleshooting

3.1 LLM-powered configuration validation

3.2 AI in the network CICD pipeline

3.3 AI-assisted log and telemetry analysis

3.4 ChatOps with AI for network teams

Lesson 4: Documentation, Knowledge Management, and Project Design

4.1 Auto-generating network documentation

4.2 Building a network knowledge base with RAG

4.3 Designing your first AI-augmented network project

4.4 From prototype to production deployment

Lesson 5: Best Practices, Security, and the Future

5.1 When not to use AI in network operations

5.2 Data quality, bias, and hallucination risks

5.3 Securing AI-powered network workflows

5.4 The future of NetDevOps with AI

Updates

Submit Errata