AI for Network Engineers
Published by Pearson
AI Fundamentals for building AI-enabled Networks
- Focus on practical implementation, bridging the gap between theoretical AI concepts and real-world network engineering applications.
- Cross-functional expertise, delivered by instructor with dual backgrounds in both network engineering and AI/ML implementation.
- Foundational AI concepts, delivered keeping network engineers in mind and illustrating the challenges that they will be facing when designing AI-enabled networks.
This specialized training course equips network engineers with practical AI skills directly applicable to enterprise networking challenges. Moving beyond theoretical concepts, participants will work with actual network datasets to implement AI-driven solutions for automation, anomaly detection, and predictive maintenance. The course combines fundamental AI/ML concepts with hands-on labs using popular tools such as TensorFlow, PyTorch, and network-specific AI platforms to solve common networking problems including traffic analysis, security threat detection, and intelligent capacity planning.
In this 2-day training program, Day 1 focuses on foundational AI concepts for network engineers, data collections and preparation, understanding basic ML models for network engineers. Day 2 covers advanced topics such as Deep Learning for network analysis, NLP for network operations, and understanding LLMs for advanced and AI integrated automation.
What you’ll learn and how you can apply it
- Implement basic machine learning models for network traffic anomaly detection and performance prediction.
- Develop automated network configuration systems enhanced with AI-driven decision support.
- Apply natural language processing techniques to network logs and alerts for improved troubleshooting.
- Integrate AI tools with existing network monitoring platforms to create predictive maintenance workflows.
This live event is for you because...
- You are a network engineer, architect, or operations specialist who recognizes the growing impact of AI on networking technologies and wants to stay ahead of industry trends.
- You are looking to enhance your organization's network operations with AI capabilities or advance your career by developing cross-functional expertise, this course provides practical skills applicable to enterprise environments.
- You are a networking professional with strong foundational knowledge who wants to extend your capabilities into AI/ML without needing extensive data science prerequisites.
Prerequisites
- Working knowledge of enterprise networking concepts and protocols (routing, switching, security)
- Familiarity with at least one network automation tool or framework (Ansible, Python, Terraform, etc.)
- Basic understanding of programming concepts (variables, functions, loops)
- Experience with network monitoring and data collection systems
Recommended Preparation
- Attend: AI-Enabled Programming, Networking, and Cybersecurity by Omar Santos
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Day 1
Segment 1: Foundations of AI for Network Engineering (70 Minutes)
- Introduction to AI/ML concepts relevant to networking
- Overview of key AI use cases in enterprise networks
- Network data types and their suitability for machine learning
Q&A and Break (10 minutes)
Segment 2: Network Data Collection and Preparation (70 Minutes)
- Techniques for collecting and structuring network telemetry data
- Data cleansing and normalization for network datasets
- Feature engineering for network traffic analysis
Q&A and Break (10 minutes)
Segment 3: Basic ML-Models for Network Engineers (70 Minutes)
- Supervised learning algorithms for network analysis
- Unsupervised learning techniques for pattern discovery
- Evaluation metrics for network-focused models
Q&A and Break (10 minutes)
Day 2
Segment 4: Deep Learning for Network Analysis (70 Minutes)
- Neural networks for complex network pattern recognition
- Recurrent networks for sequence analysis in network logs
- CNN applications in network traffic visualization
Q&A and Break (10 minutes)
Segment 5: NLP for Network Operations (70 Minutes)
- Processing network logs with NLP techniques
- Automated ticket classification and routing
- Sentiment analysis for user experience monitoring
Q&A and Break (10 minutes)
Segment 6: Large Language Models (LLMs) (70 Minutes)
- Overview of LLMs
- Types of LLMs
- AI Integrated Network Automation
Q&A and Wrap up (10 minutes)
Your Instructor
Vinit Jain
Vinit Jain, CCIE No. 22854 (R&S, SP, Security & DC), is a CTO / Principal Engineer at Iraitech Innovations & Technologies Pvt. Ltd. Prior to that, he was working as a Sr. Technical Leader for Network Engineering at Cisco focusing on architecting network infrastructure for edge computing solutions. He also worked as a Network Development Engineer at Amazon as part of Amazon’s backbone network operations team and as a technical leader at Cisco Technical Assistance Center (TAC), providing escalation support in routing and data center technologies. Vinit is a speaker at various networking forums, including Cisco Live! events. He has co-authored several Cisco Press books, Apress Books, and video courses with Cisco Press. In addition to his CCIEs, Vinit holds multiple certifications related to programming and databases, and he is also a CEH. Vinit graduated from Delhi University in mathematics and earned a master's in information technology from Kuvempu University in India. Visit Vinit's youtube channel to learn more.