AI+ Network Practitioner™

Validate Your Expertise in Networking: Harness AI for Automation, Security, and Next-Generation Efficiency

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Certificate code

AT-510

Duration

Instructor-Led: 5 days (live or virtual) Self-Paced: 40 hours of content

Exam format

50 questions, 70% passing, 90 minutes, online proctored exam

Includes

Instructor-led OR Self-paced course + Official exam + Digital badge

Overview Pricing Opportunities Course Modules Tools

Our pricing

Technical

Technical course options

Instructor-led pricing appears only when we have an open cohort for the programme.

Self-paced

NGN 283,500

NGN 358,500

Independent learning with certification preparation support.

Programme overview

Certification preparation delivered with Stackron Academy support

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.

Certification modules

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 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

Module 11: Capstone Project - End-to-End AI Network Operations

11.1 Capstone Objective 11.2 Capstone Scenario

Optional 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

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.

Who should enroll

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.