This three-day course is designed for systems with facilities for basic treatment and.or pressure zones, booster stations, storage tanks, fire protection, disinfection, nonresidential consumers, cross-connection potential, demand variations, and more.

This course is designed for operators of distribution systems serving greater than 1,000 people. Topics include pressure zones, booster stations, storage tanks, fire protection and disinfection.

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: 6/8/26 – 8/30/26

2: Linux & Cloud Foundations 7/6/26 - 9/27/26 or 8/31/26 - 11/22/26

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: 6/8/26 – 8/30/26

2: Cloud Native App Deployment  7/6/26 - 9/27/26 or 8/31/26 - 11/22/26