Opleiding: AI+ Network Practitioner™ eLearning

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.

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. 

• Comprehensive KnowledgeCovers fundamental networking concepts and advanced AI-driven technologies like SDN and NFV.
• AI-Powered EfficiencyExam assesses knowledge of how AI can optimize network performance, automation, and security
• Practical Application AssessmentExam includes scenario-based questions for real-world application of AI in networking.
• Future-Ready SkillsValidates competency to adapt to the rapidly evolving AI-enhanced networking landscape.
• Leadership OpportunitiesValidates competency for leadership roles in the AI and networking fields.

• 1.1 Basic Networking Concepts
• 1.2 Network Infrastructure and Design
• 1.3 Introduction to Network Security
• 1.4 AI Workload Networking Overview

• 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

• 3.1 Network Virtualization and Cloud Networking Models
• 3.2 SD-WAN and Hybrid Multi-Cloud Connectivity
• 3.3 SASE and SSE with AI

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 11.1 Capstone Objective
• 11.2 Capstone Scenario

• 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

• Ansible
• Puppet
• Chef
• REST APIs
• NETCONF
• Kubernetes
• OpenStack
• GNS3
• Cisco Packet Tracer
• VMware

Online proctored exam included, with one free retake.
Exam format: 50 questions, 70% passing, 90 minutes, online proctored exam

Access to all materials and exams is provided for 365 days after delivery.

De OCICT maatwerk-methode houdt in dat op basis van de behoefte van de klant - en indien nodig na een intakegesprek - een individueel opleidingstraject wordt opgesteld. De trainingen worden veelal individueel gevolgd, zodat iedere training een maatwerktraject is. Klassikale trainingen zijn natuurlijk mogelijk, mits ze aansluiten bij de leerbehoeften van de deelnemers / opdrachtgever.

Alle onderdelen van de training kennen een voortdurende wisselwerking tussen theorie en praktijk. De inhoud van de training kan vooraf per cursist worden aangepast en tijdens de cursus worden bijgesteld. De focus ligt op maatwerk: het traject wordt afgestemd op de doelstellingen van de deelnemer(s) en/of opdrachtgever, ongeacht of het individueel of klassikaal wordt aangeboden.