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

Additional Courses in AI Infrastructure Track:

1: AI Networking Fundamentals: 9/7/26 – 11/29/26

3: AI Infrastructure & Security 10/19/26 - 1/17/27 or 2/22/27 - 5/23/27

Students will have the opportunity to learn the theory and hands-on skills to gain the foundational knowledge of Hydronic and Forced Air Heating systems. The successful completion of the 75-hour HVAC module I course will allow and prepare students to take the more advanced 75-hour HVAC module II course.

Topics include the following:

• Safety
• HVAC Tools
• Mathematics for HVAC
• Theory of Electricity - I
• Blueprints and wiring diagrams
• Soldering, Braizing and Welding
• Piping Materials and Fittings
• HVAC Career Opportunities
• Introduction to HVAC Equipment - Boilers, Furnaces & AC
• Fundamentals of HVAC Equipment- Boilers, Furnaces, Valves and Meters
• Radiant Heat Systems
• Troubleshooting HVAC Equipment
• Customer Service
• Soft Skills
• Work Ethic

The Skilled Trades section prepares students for real-world work in key areas of construction. Learn the essentials of residential electrical systems, HVAC basics, and plumbing, including DWV systems, plastic and copper piping, and fittings. Students also gain hands-on experience in drywall and interior finishing, while earning valuable credentials like Lift Certification and First Aid/CPR/AED. Build specialized skills that help you stand out in the trades. 

Scholarships may be available.  If interested, please contact Shannon at 716-338-1338 or shannonrinow@sunyjcc.edu prior to registering.

This course is one third of our Construction Technology Program.  If you are interested in taking the "Core Safety" and "Foundational" sections as well, please use the link below to register for the full program and receive a discount. 

https://jcc.coursestorm.com/course/construction-technology-full-program

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 

Additional Courses in AI Infrastructure Track:

1: AI Networking Fundamentals: 9/7/26 – 11/29/26

2: Linux & Cloud Foundations 10/5/26 - 12/27/26 or 11/30/26 - 2/21/27

AI Development Track   

The AI Development Track is a hands-on journey that takes students from Python fundamentals all the way to becoming a professional AI Engineer. Over three intensive courses, you'll master the complete AI development lifecycle and have a portfolio of production-ready applications. This program is designed for those ready to move beyond using AI to building the intelligent applications of tomorrow.  

Applied AI, Agents & Automation 

Course 3 of 3

Engineer intelligent systems—not just models. This advanced course pushes you into the world of production‑grade AI engineering. You’ll build autonomous agents, orchestrate workflows with both code and low‑code tools, fine‑tune open‑source LLMs, and implement Retrieval‑Augmented Generation (RAG) for long‑term memory. Finally, you’ll containerize a multi‑service AI system and deploy it on a GPU‑accelerated Kubernetes cluster. This is where developers become AI engineers. Ideal for: Software developers who want to specialize in Artificial Intelligence and Large Language Model (LLM) integration. Data scientists looking to operationalize their models and move them from notebooks to production web services. DevOps engineers who need to understand the specific infrastructure requirements of AI workloads, including GPU orchestration. Graduates of Course 2  ready to apply their full-stack and DevOps skills to the cutting edge of AI technology. 

Prerequisite Skills  

This is an advanced course that combines software engineering with data science. To ensure success, students should have: 

Full-Stack Development Experience: Strong proficiency in Python, web frameworks (Flask/FastAPI), and API design, as covered in Course 2: Cloud-Native App Deployment. 

Containerization & DevOps Skills: A solid understanding of Docker, GitLab CI/CD pipelines, and basic Kubernetes concepts. 

Database Knowledge: Comfort with SQL and interacting with databases programmatically. 

Understanding of Machine Learning Basics: While we teach advanced techniques, a conceptual understanding of what a model is (from Course 1: Python AI Fundamentals or equivalent experience) is helpful. 

Additional Courses in AI Development Track:

1: Python AI Fundamentals: 9/7/26 – 11/29/26

2: Cloud Native App Deployment  10/5/26 - 12/27/26 or 11/30/26 - 2/21/27