Courses AI certifications (AI CERTs®)

AI+ Network Practitioner™

  • Price 279 EUR + VAT
  • E-learning Start any time
  • Duration 40 hours of content, self-paced
  • Language English
  • Topic AI Security

How you can take it

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Overview

What the course covers

The course, the materials and the exam are in English. The description below is the original AI CERTs® text.

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.

Audience

Who is it for

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 in 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.

In the price

What the price includes

  • Self-paced online course with the full learning material
  • The official AI CERTs® exam
  • Digital badge after passing the exam

Course content

Syllabus

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: 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

Exam

About the exam

  • 50 Questions
  • 90 min Time limit
  • 70% Pass mark
  • Online proctored exam

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