AI Networking Fundamentals (Online) - 3281

AI Infrastructure Track  

The AI Infrastructure Track prepares students to design, build, and manage the modern systems behind AI deployment. Across three hands-on courses, students progress from foundational networking to full cloud ‑native‑ orchestration, gaining the practical skills needed to support real-world AI workloads.  

Linux & Cloud Foundations 

Course 2 of 3 

Turn your network into a living, breathing infrastructure. 
Learn the Linux, virtualization, and automation skills powering modern cloud environments. From user management and shell scripting to provisioning cloud-style virtual machines, you'll build and secure real services—including identity management, databases, and web apps. The ideal jump from networking into full system administration.  

Ideal for: Network technicians who want to expand their skill set into system administration.· IT professionals seeking to formalize their Linux knowledge for cloud and DevOps roles.· Students who have completed Course 1 and are ready to build the services that run on the network. 

Prerequisite Skills:  

This course builds directly upon the networking foundation. To ensure success, students should have: 

Completion of Course 1 (AI Networking Fundamentals) or equivalent networking knowledge: 

Understanding of IP addressing, subnetting, and CIDR notation 

Familiarity with core network services (DNS, DHCP) 

Knowledge of routing concepts and static routes 

Experience configuring firewalls and understanding basic security principles 

Basic Command-Line Literacy: 

Comfort opening a terminal and navigating directories (cd, ls, mkdir) 

Ability to view files (cat, less) and edit simple text files 

Understanding of Virtualization Concepts: 

Conceptual knowledge of what a Virtual Machine (VM) is and how it differs from physical hardware 

Basic understanding of hypervisors (like Proxmox, VMware, or VirtualBox) 

General Computer Literacy: 

Comfort installing operating systems (Xubuntu) and software 

Ability to troubleshoot basic technical issues independently 

AI Infrastructure Track  

The AI Infrastructure Track prepares students to design, build, and manage the modern systems behind AI deployment. Across three hands-on courses, students progress from foundational networking to full cloud ‑native‑ orchestration, gaining the practical skills needed to support real-world AI workloads.  

Linux & Cloud Foundations 

Course 2 of 3 

Turn your network into a living, breathing infrastructure. 
Learn the Linux, virtualization, and automation skills powering modern cloud environments. From user management and shell scripting to provisioning cloud-style virtual machines, you'll build and secure real services—including identity management, databases, and web apps. The ideal jump from networking into full system administration.  

Ideal for: Network technicians who want to expand their skill set into system administration.· IT professionals seeking to formalize their Linux knowledge for cloud and DevOps roles.· Students who have completed Course 1 and are ready to build the services that run on the network. 

Prerequisite Skills:  

This course builds directly upon the networking foundation. To ensure success, students should have: 

Completion of Course 1 (AI Networking Fundamentals) or equivalent networking knowledge: 

Understanding of IP addressing, subnetting, and CIDR notation 

Familiarity with core network services (DNS, DHCP) 

Knowledge of routing concepts and static routes 

Experience configuring firewalls and understanding basic security principles 

Basic Command-Line Literacy: 

Comfort opening a terminal and navigating directories (cd, ls, mkdir) 

Ability to view files (cat, less) and edit simple text files 

Understanding of Virtualization Concepts: 

Conceptual knowledge of what a Virtual Machine (VM) is and how it differs from physical hardware 

Basic understanding of hypervisors (like Proxmox, VMware, or VirtualBox) 

General Computer Literacy: 

Comfort installing operating systems (Xubuntu) and software 

Ability to troubleshoot basic technical issues independently 

AI Infrastructure Track  

The AI Infrastructure Track prepares students to design, build, and manage the modern systems behind AI deployment. Across three hands-on courses, students progress from foundational networking to full cloud ‑native‑ orchestration, gaining the practical skills needed to support real-world AI workloads.  

Course 3: AI Infrastructure & Security 

Course 3 of 3 

Orchestrate the systems that power AI. Take your skills to the next level by building a multi-node Kubernetes cluster capable of running GPU accelerated AI workloads. You’ll master cloud native security, resilient storage, and professional observability tools—then integrate actual GPU hardware into a hybrid cluster. This is your launchpad into AI operations, DevOps, and scalable infrastructure engineering.  

Ideal for:  System Administrators and DevOps engineers looking to specialize in Kubernetes and cloud-native technologies. Students who have completed Courses 1 and 2 and are ready to tackle the challenges of orchestrating a production-like environment. IT professionals wanting to gain the in-demand skills of AI infrastructure management and GPU orchestration. 

Prerequisite Skills:  

This is the advanced culmination of the Infrastructure track. To ensure success, students should have: 

Completion of Courses 1 and 2 or equivalent combined knowledge: 

From Course 1, AI Networking Fundamentals: Solid networking fundamentals, including subnet design, routing, DNS/DHCP configuration, firewall rules, NAT, and network troubleshooting. 

From Course 2, Linux and Cloud Foundations: Proficiency in Linux command-line administration, user and permission management, systemd service management, shell scripting, and virtual machine management. 

Basic Containerization Knowledge: 

Conceptual understanding of what containers are and how they differ from VMs 

Familiarity with basic Docker commands (docker run, docker build, docker ps) is helpful but not strictly required 

Understanding of Linux Services and Networking: 

Experience installing and configuring services on Linux (web servers, databases) 

Ability to configure network interfaces and troubleshoot connectivity issues on Linux servers 

Familiarity with Git and Version Control: 

Understanding of basic Git workflows (clone, commit, push) for managing configuration files 

Security Awareness: 

Understanding of basic security principles (authentication, authorization, least privilege) 

Familiarity with SSH key-based authentication 

AI Infrastructure Track  

The AI Infrastructure Track prepares students to design, build, and manage the modern systems behind AI deployment. Across three hands-on courses, students progress from foundational networking to full cloud ‑native‑ orchestration, gaining the practical skills needed to support real-world AI workloads.  

Course 3: AI Infrastructure & Security 

Course 3 of 3 

Orchestrate the systems that power AI. Take your skills to the next level by building a multi-node Kubernetes cluster capable of running GPU accelerated AI workloads. You’ll master cloud native security, resilient storage, and professional observability tools—then integrate actual GPU hardware into a hybrid cluster. This is your launchpad into AI operations, DevOps, and scalable infrastructure engineering.  

Ideal for:  System Administrators and DevOps engineers looking to specialize in Kubernetes and cloud-native technologies. Students who have completed Courses 1 and 2 and are ready to tackle the challenges of orchestrating a production-like environment. IT professionals wanting to gain the in-demand skills of AI infrastructure management and GPU orchestration. 

Prerequisite Skills:  

This is the advanced culmination of the Infrastructure track. To ensure success, students should have: 

Completion of Courses 1 and 2 or equivalent combined knowledge: 

From Course 1, AI Networking Fundamentals: Solid networking fundamentals, including subnet design, routing, DNS/DHCP configuration, firewall rules, NAT, and network troubleshooting. 

From Course 2, Linux and Cloud Foundations: Proficiency in Linux command-line administration, user and permission management, systemd service management, shell scripting, and virtual machine management. 

Basic Containerization Knowledge: 

Conceptual understanding of what containers are and how they differ from VMs 

Familiarity with basic Docker commands (docker run, docker build, docker ps) is helpful but not strictly required 

Understanding of Linux Services and Networking: 

Experience installing and configuring services on Linux (web servers, databases) 

Ability to configure network interfaces and troubleshoot connectivity issues on Linux servers 

Familiarity with Git and Version Control: 

Understanding of basic Git workflows (clone, commit, push) for managing configuration files 

Security Awareness: 

Understanding of basic security principles (authentication, authorization, least privilege) 

Familiarity with SSH key-based authentication