From Kubernetes to Microservices, CI/CD, and Service Mesh in Three Hours

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

We will summarize the core theories spanning from Machine Learning and Deep Learning to Generative AI in one go. And by implementing various Generative AI applications with easy Python code, you can take your first step as an AI developer.

With the price of a chicken combo, you'll learn the deployment process in order: Spring Boot manual deployment -> Docker deployment -> Github Action CI/CD!

Do you want to effectively use GitHub Actions in real life? This course introduces practical CI/CD scenarios, modularization and version management methods, and also teaches you how to deploy in a cloud environment. Let's learn the most realistic way to use GitHub Actions based on practical experience!

This course is designed to help you quickly learn server zero-downtime deployment methods using Nginx, Docker, and Spring Boot through hands-on practice. Learn Blue-Green deployment, which is frequently used in real-world scenarios, faster than anyone else!

Learn how to model data in a distributed database environment.

I've created a 'Kafka Introduction' course that even non-majors can easily understand and apply directly in real-world situations!

Instead of long and boring theories, you can learn them one by one through short practical exercises, and as you flesh them out one by one, you will soon be able to experience the overall architecture composition.

Regardless of the field! This is a lecture that covers the core fundamentals that developers must know in practice in an easy + code-oriented way. I highly recommend this lecture to all junior developers and job seekers who want to grow.

You can quickly experience the process of deploying a sample e-commerce service to AWS and continuously improving it into a robust and efficient environment.

This is a 'Practical Linux Troubleshooting Course' where you analyze and solve problems together with generative AI, based on a Docker practice environment that reproduces 5 representative failures that occur in actual Linux operating environments. Even those who were overwhelmed about where to start examining problems can learn the problem-solving approach and thought process of field engineers through AI guidance.

This course is Part 3 of the Full Stack lecture series, designed to help you solidly master the latest server technologies and Docker. The course is structured to enable you to practically incorporate Docker into your tech stack by meticulously testing Docker's various options, learning related technologies such as Linux that are necessary for understanding Docker, and building actual complex services.

When you're doing backend development, at some point you face this dilemma. As services grow and traffic increases, there comes a moment when simple REST communication and CRUD structures feel limiting. I experienced the same problem in real-world work and went through many trials and errors between performance and structural complexity. The solution I chose in that process was NATS. Its simple configuration based on a single binary, ultra-low latency performance in microseconds, and the fact that it provides just the right amount of essential features for real work—from Pub/Sub to Queue Groups and JetStream—made it a very practical choice in actual service environments. This course is not about listing NATS features. Based on the problems I faced in real work, I explain with code and flow diagrams which situations led me to choose NATS, why, and how I designed the architecture. This is a course designed to help everyone—from those new to messaging systems, to backend developers preparing for jobs, to senior developers contemplating better architectures—understand distributed systems and grow to the next level.

Packed with LLM Fine-Tuning Know-how, learned from Silicon Valley LLM Project Practitioners.

Let's implement a web service modeled after Pinterest's unique card-type layout design using Django, Python's representative web framework.

In this course, you will be able to create a Reddit website from scratch, step by step.

Build a local LLM with RAG-based Q&A system in just 2 hours of hands-on practice using Docker + ollama + R2R framework. (Suitable for in-house/internal Q&A systems, personal portfolios, AI-powered work capabilities, and even commercial services)

Learn from the beginning how to set up a development environment that even an ordinary office worker can easily understand. It is practice-oriented and focuses on students learning various development tools such as Vim, VS Code, Git, Docker, and remote development. You can learn how to build an efficient development environment by learning step-by-step from the basic usage of each development tool to advanced functions.

Ghost In the N8N - AI Automation Workflow (AI automation flow through the N8N framework) You will gain the foundational knowledge needed for practical work & business automation in the AI era, along with guides you can use immediately in real-world applications!

This course is built from real-world experience designing and deploying production-grade LLM applications on Azure. Rather than focusing on toy examples or isolated notebooks, I guide students through the exact challenges they face when moving from experimentation to real systems: data ingestion, vector storage, service orchestration, cloud deployment, and security. Throughout the course, I break down complex Azure services into clear, practical steps, explain why each architectural decision is made, and show how components work together in a scalable system. Students learn by building, debugging, and deploying a complete LangChain-based application, gaining confidence in solving real infrastructure and application-level problems.

What is DevSecOps? Configure a CI build environment and an environment that automatically performs SAST checks during build

A solo IT startup founder and solo entrepreneur's experience distilled into an honest, straightforward, intuitive, and deep React + Spring Boot course (Docker, AWS, GitHub Actions) I just unified everything into this one course.