AI Networking and Coding

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. 

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. 

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.  

Cloud‑Native Application Deployment 

Course 2 of 3. 

Turn your AI ideas into real, cloud‑powered applications. 
Learn how modern AI-backed web apps are built and shipped. You’ll transform a Python script into a full-stack web application with Flask, SQLAlchemy, and a hand‑coded front end—and then take it all the way to the cloud. Containerize with Docker, automate with GitLab CI/CD, and deploy to Kubernetes. By the end, your work isn’t just running locally… it’s live. Ideal for Python developers looking to transition into web development or backend engineering. IT professionals who want to modernize their skillset with containerization and orchestration technologies like Docker and Kubernetes. Graduates of Course 1 who want to see their code come to life as a live, interactive web application. Aspiring DevOps engineers seeking a practical, project-based introduction to CI/CD pipelines. 

Prerequisite Skills  

This course focuses on web technologies and cloud infrastructure. To ensure success, students should have: 

Python Proficiency: A solid understanding of Python syntax, functions, and Object-Oriented Programming (OOP), as covered in Course 1: Python AI Fundamentals

Command-Line Literacy: Comfort with navigating the file system, running scripts, and managing files via a terminal. 

Git Fundamentals: Understanding of basic version control concepts like cloning, committing, and pushing code. 

Logical Thinking: The ability to understand how data flows between a client (browser), a server (API), and a database. 

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.  

Cloud‑Native Application Deployment 

Course 2 of 3. 

Turn your AI ideas into real, cloud‑powered applications. 
Learn how modern AI-backed web apps are built and shipped. You’ll transform a Python script into a full-stack web application with Flask, SQLAlchemy, and a hand‑coded front end—and then take it all the way to the cloud. Containerize with Docker, automate with GitLab CI/CD, and deploy to Kubernetes. By the end, your work isn’t just running locally… it’s live. Ideal for Python developers looking to transition into web development or backend engineering. IT professionals who want to modernize their skillset with containerization and orchestration technologies like Docker and Kubernetes. Graduates of Course 1 who want to see their code come to life as a live, interactive web application. Aspiring DevOps engineers seeking a practical, project-based introduction to CI/CD pipelines. 

Prerequisite Skills  

This course focuses on web technologies and cloud infrastructure. To ensure success, students should have: 

Python Proficiency: A solid understanding of Python syntax, functions, and Object-Oriented Programming (OOP), as covered in Course 1: Python AI Fundamentals

Command-Line Literacy: Comfort with navigating the file system, running scripts, and managing files via a terminal. 

Git Fundamentals: Understanding of basic version control concepts like cloning, committing, and pushing code. 

Logical Thinking: The ability to understand how data flows between a client (browser), a server (API), and a database. 

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.  

Course 1: Python AI Fundamentals 

Course 1 of 3. Recommended first course.

Build your future in AI—starting with code. 
This hands-on course takes you from basic programming knowledge to writing real, production ready Python applications. You’ll master professional tools like Git and VS Code, build your first commandline AI app, and complete a full machine learning project using industry standard libraries like Pandas, Scikitlearn, and PyTorch. The perfect launchpad for anyone entering AI development, data science, or automation.  

Ideal for: IT and Network professionals looking to transition into a data-focused or AI development role. Analysts and researchers who want to move from analyzing data in spreadsheets to building predictive models. Developers from other domains who want to add machine learning and data science skills to their toolkit. Ambitious tech enthusiasts who want a comprehensive, project-based introduction to modern AI development. 

Prerequisite Skills 

This course is comprehensive and fast-paced, designed to take you from programming fundamentals to AI fundamentals in 10 weeks. To ensure success, students should have: 

Basic Programming Literacy: You understand the core concepts of programming in any language (e.g., variables, loops, if/else statements). Experience with Excel formulas, VBA, JavaScript, or any scripting language is ideal. 

Logical Reasoning Skills: The ability to think through a step-by-step process to solve a problem. The course begins by teaching you how to formalize this with pseudocode. 

Comfort with Installing Software: You can download and install applications like Python and a code editor (like VS Code) on your own computer and are not intimidated by configuration files. 

Basic File Management: You are comfortable creating, finding, and moving files and folders on your operating system (Windows, macOS, or Linux). 

A "Can-Do" Mindset: This is a challenging but rewarding course. A willingness to engage with complex topics, troubleshoot errors, and persist through challenges is essential for success. 

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