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AI+ Network Practitioner™
USD$495.00 (GST excl.)
Validate Your Expertise in Networking: Harness AI for Automation, Security, and Next-Generation Efficiency
This certification validates professional knowledge and competency in the combination of artificial intelligence and current networking technologies. The exam assesses understanding of fundamental networking concepts, newer technologies such as SDN and NFV, and how AI can enhance network efficiency. Key focus areas include AI-powered network automation, orchestration, and security upgrades. The exam includes scenario-based questions covering emerging developments in AI-enhanced networking, validating candidate readiness for leadership roles in this rapidly evolving sector.
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Duration
Instructor-Led: 5 days (live or virtual)
Self-Paced: 40 hours of content
Self-Paced: 40 hours of content
Format
Online, self-paced
Exam
50 questions, 70% passing, 90 minutes, online proctored exam
Overview
This certification validates professional knowledge and competency in the combination of artificial intelligence and current networking technologies. The exam assesses understanding of fundamental networking concepts, newer technologies such as SDN and NFV, and how AI can enhance network efficiency. Key focus areas include AI-powered network automation, orchestration, and security upgrades. The exam includes scenario-based questions covering emerging developments in AI-enhanced networking, validating candidate readiness for leadership roles in this rapidly evolving sector.
Prerequisites
Basic networking, Python, AI/ML fundamentals, and familiarity with network management tools.
What you’ll learn
- Networking Professionals: Looking to advance your skills by integrating AI into network design, automation, and security to stay ahead in a competitive field.
- AI Enthusiasts: Interested in applying AI technologies like ML and automation Specifically, networking domains.
- IT Specialists: Focused on exploring the intersection of cloud computing, IoT, and AI to optimize infrastructure and network performance.
- Students and Fresh Graduates: Pursuing careers in AI, cybersecurity, or networking and wanting to gain hands-on experience and credentials to strengthen their professional profile.
- Cybersecurity Analysts: Seeking to utilize AI-driven solutions for threat detection, network protection, and predictive analytics.
- System Administrators: Eager to transition into roles involving network automation, orchestration, and AI-based tools for managing complex systems.
- Tech Innovators and Researchers: Interested in exploring emerging trends like 5G, edge computing, or blockchain in AI-enhanced networking.
Why this certification matters
- Comprehensive Knowledge Covers fundamental networking concepts and advanced AI-driven technologies like SDN and NFV.
- AI-Powered Efficiency Exam assesses knowledge of how AI can optimize network performance, automation, and security
- Practical Application Assessment Exam includes scenario-based questions for real-world application of AI in networking.
- Future-Ready Skills Validates competency to adapt to the rapidly evolving AI-enhanced networking landscape.
- Leadership Opportunities Validates competency for leadership roles in the AI and networking fields.
Tools you’ll use
Ansible
Puppet
Chef
REST APIs
NETCONF
Kubernetes
OpenStack
GNS3
Cisco Packet Tracer
VMware
Learning Modules
12 modules
1Module 1: Enterprise Networking Foundations & AI Workload Impact
1.1 Basic Networking Concepts
1.2 Network Infrastructure and Design
1.3 Introduction to Network Security
1.4 AI Workload Networking Overview
1.2 Network Infrastructure and Design
1.3 Introduction to Network Security
1.4 AI Workload Networking Overview
2Module 2: Advanced Routing, Switching, and Data Center/AI Fabric Networking
2.1 Advanced Routing and Switching
2.2 Data Center and AI Infrastructure Networking
2.3 High-Performance AI Fabric Considerations
2.4 Quality of Service (QoS) for Application and AI Workloads
2.2 Data Center and AI Infrastructure Networking
2.3 High-Performance AI Fabric Considerations
2.4 Quality of Service (QoS) for Application and AI Workloads
3Module 3: Cloud Networking, SASE, and Hybrid Connectivity
3.1 Network Virtualization and Cloud Networking Models
3.2 SD-WAN and Hybrid Multi-Cloud Connectivity
3.3 SASE and SSE with AI
3.2 SD-WAN and Hybrid Multi-Cloud Connectivity
3.3 SASE and SSE with AI
4Module 4: Wi-Fi 7, Edge AI & IoT Networking
4.1 Wi-Fi 7 and AI-Driven RF Optimization
4.2 Edge Computing, Fog Networking and IoT Models
4.3 Edge AI and Small Language Models
4.4 Wi-Fi 7 + Edge AI Use Cases and Architecture
4.2 Edge Computing, Fog Networking and IoT Models
4.3 Edge AI and Small Language Models
4.4 Wi-Fi 7 + Edge AI Use Cases and Architecture
5Module 5: AI & Machine Learning Foundations for Network Engineers
5.1 AI and Machine Learning Fundamentals
5.2 AI-Driven Network Optimization
5.3 Operational Limits of AI Recommendations
5.4 Predictive Network Maintenance
5.2 AI-Driven Network Optimization
5.3 Operational Limits of AI Recommendations
5.4 Predictive Network Maintenance
6Module 6: Generative AI, RAG, and Prompt Engineering for Operations
6.1 Generative AI and LLM Concepts for Network Operations
6.2 RAG (Retrieval-Augmented Generation) for Network Knowledge
6.3 Prompt Engineering for Network Engineers
6.2 RAG (Retrieval-Augmented Generation) for Network Knowledge
6.3 Prompt Engineering for Network Engineers
7Module 7: Network Automation, IaC, and Agentic AI Workflows
7.1 Fundamentals of Network Automation & Infrastructure as Code (IaC)
7.2 Network APIs and Programmability
7.3 Agentic AI, Function Calling, and MCP
7.4 ChatOps and Operational Workflows
7.5 Use-Cases and Case Studies
7.2 Network APIs and Programmability
7.3 Agentic AI, Function Calling, and MCP
7.4 ChatOps and Operational Workflows
7.5 Use-Cases and Case Studies
8Module 8: AI-Enhanced Network Security and Zero Trust
8.1 AI-Enhanced Threat Detection
8.2 Secure Network Design and Zero Trust
8.3 SIEM, SOC, and AI-Assisted Security Operations
8.4 Adversarial AI and AI Security Risks
8.5 Use-Cases and Case Studies
8.2 Secure Network Design and Zero Trust
8.3 SIEM, SOC, and AI-Assisted Security Operations
8.4 Adversarial AI and AI Security Risks
8.5 Use-Cases and Case Studies
9Module 9: Modern Observability: eBPF, OpenTelemetry, and AIOps
9.1 Modern Observability Foundations (Metrics, Logs, and Traces)
9.2 eBPF for Deep Network Visibility
9.3 OpenTelemetry and Streaming Telemetry Standards
9.4 AIOps: Alert Correlation, Noise Reduction, and Root Cause Support
9.5 Use-Cases and Case Studies
9.2 eBPF for Deep Network Visibility
9.3 OpenTelemetry and Streaming Telemetry Standards
9.4 AIOps: Alert Correlation, Noise Reduction, and Root Cause Support
9.5 Use-Cases and Case Studies
10Module 10: AI Governance, Responsible AI, and Sustainable Networking
10.1 AI Governance and Responsible Network Operations
10.2 Privacy, Data Handling, and Bias in Network AI
10.3 Sustainable/Green Networking with AI
10.4 Future Network Operations
10.5 Use-Cases and Case Studies
10.2 Privacy, Data Handling, and Bias in Network AI
10.3 Sustainable/Green Networking with AI
10.4 Future Network Operations
10.5 Use-Cases and Case Studies
11Module 11: Capstone Project – End-to-End AI Network Operations
11.1 Capstone Objective
11.2 Capstone Scenario
11.2 Capstone Scenario
12Optional Module: Optional Module: AI Agents For Network
1.1 What Are AI Agents
1.2 Applications and Trends of AI Agents in Network Intelligence
1.3 How Does an AI Agent Work
1.4 Characteristics of AI Agents
1.5 Types of AI Agents
1.2 Applications and Trends of AI Agents in Network Intelligence
1.3 How Does an AI Agent Work
1.4 Characteristics of AI Agents
1.5 Types of AI Agents
| Format | Online, self-paced |
|---|---|
| Duration | Instructor-Led: 5 days (live or virtual) |
| Exam | 50 questions, 70% passing, 90 minutes, online proctored exam |
| Included | Instructor-led OR Self-paced course + Official exam + Digital badge |
| Prerequisites | Basic networking, Python, AI/ML fundamentals, and familiarity with network management tools. |
| Delivery | Projects & case studies |
| Outcome | Industry-recognized credential + hands-on experience |
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