From Deployment to Production Incident Response - GitOps, Observability, and AI Agents

From Docker, Kubernetes to microservices, CI/CD, and service mesh, you can easily learn not only the basic theory but also the actual application implementation through examples. By organizing only the core contents necessary for actual use, it provides a starting point for learning about the element technologies covered in the lecture.

Running MongoDB on Kubernetes is different from simply putting it in a container. There are many things you can only learn by dealing with storage, networking, permissions, and high availability firsthand. This course covers the entire process of configuring and operating MongoDB using the PSMDB (Percona Server for MongoDB) Operator in an AWS EKS environment. The goal is to directly build a production-ready configuration, from cluster setup to Replica Set configuration, backup, and monitoring.

Implement Kakao and Google login using Spring Boot. JWT token-based login will be implemented. A simple interface will be constructed using Vue 3.

Developing an 'Autonomous' Premium Reservation Agent Backend Using Spring AI + JPA + MySQL Beyond Chatbots to Agents: The Standard of Tool Calling and Prompt Engineering

Learning about various communication techniques and understanding their differences helps you develop the ability to select and implement the most appropriate communication method.

This course is designed for developers, DevOps engineers, cloud engineers, and IT professionals who want to master advanced Kubernetes concepts used in real-world production environments. Throughout the course, students will engage in hands-on projects and real-world deployment scenarios, learning how to build secure, scalable, and production-ready Kubernetes infrastructure. Rather than focusing solely on theory, this course emphasizes practical implementation, troubleshooting, and best practices utilized by modern DevOps teams. One of the biggest challenges students face when learning Kubernetes is understanding how all components interact within large-scale environments. Many learners struggle with topics such as RBAC permissions, persistent storage, namespaces, network policies, StatefulSets, Kustomize, and secure cloud deployments. This course addresses these issues step-by-step through clear explanations, guided labs, and examples that mirror actual production environments. Students will learn step-by-step how to organize Kubernetes resources, secure workloads, manage application configurations, deploy stateful applications like MongoDB, and automate multi-environment deployments using Kustomize. Additionally, the course covers how to deploy applications to Google Kubernetes Engine (GKE) and configure Ingress, TLS certificates, monitoring, and scaling strategies. By the end of this course, students will have the confidence to design, secure, deploy, and manage advanced Kubernetes environments used in professional DevOps and cloud engineering workflows.

We'll show you a quick and easy way to implement CircuitBreaker to create a fault-tolerant service.

This is a practical, hands-on course where you will learn the principles of AI Agents—going beyond simple chatbots that just answer questions to systems that can perceive situations, make judgments, and control actual systems. The curriculum is structured to provide a step-by-step understanding, starting from the differences between AI Agents and chatbots to the core Agent Loop structure: Sense-Think-Act, state management, planning, and feedback loops. Moving beyond theory, you will set up Windows and Linux development environments and gain hands-on experience with the basic structure of Kubernetes and kubectl commands. Following this, you will implement a process where an AI Agent detects server issues, analyzes the causes, and automatically resolves errors. Finally, by directly analyzing the Python code and internal logic of the Agent controlling Kubernetes, you will understand how AI Agents connect to and operate within real-world systems. From developers new to AI Agents to those who want to design Multi-Agents that control actual systems, this course allows you to learn both the principles and the practical implementation process of Agents.

Building an 'Intelligent Collaboration' Expert Agent Team Using Spring AI Router Pattern + RAG + MCP Beyond a Single Agent to Architecture: The Standard for Router Patterns and Agent Isolation Design

In this lecture, you will learn how to understand various deployment strategies and choose the right deployment method for your situation to maximize service stability. You will also get practical tips on setting up Slack alarms and deploying to an operating environment!

We will teach you how to obtain certified Kubernetes certifications such as Certified Kubernetes Administrator (CKA), Kubernetes Certified Application Developer (CKAD), and Certified Kubernetes Security Specialist (CKS).

Learn the SAGA transaction management technique for maintaining data consistency in distributed environments through a hands-on approach. You will configure three microservices using Spring Boot and Docker, and implement both Orchestration and Choreography patterns using Kafka and MySQL. Through this course, you will master core concepts applicable to real-world scenarios, including the operational principles of distributed transactions, designing compensating transactions, and handling fault recovery.

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

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.

Building your own emotional diary service with Spring AI + React It's time for our 4th project with Neo + Essential!

I want to launch a side project as a real service and study Kubernetes, but managed services from AWS/GCP (EKS, GKE, etc.) are too expensive. On the other hand, running it only on a local computer makes it difficult to experience network configurations or actual deployment environments. We will utilize the "Always Free" tier of Oracle Cloud (OCI), which offers the most powerful free benefits among existing public clouds. By installing the lightweight yet powerful k3s, we will build a Linux server and Kubernetes environment capable of hosting actual services from scratch—all at zero cost. After taking this course, you will gain a general understanding of cloud networking and possess your own Kubernetes cluster infrastructure where you can deploy and operate your applications for free.

It covers the entire cycle, starting from what logs to record to how to collect and utilize them.

⚓ Kubernetes CKAD Certification! 🙇‍♀️Are you still hesitating? 🚢Board the Ilpro-ho! We will guide you through the optimal route.

I highly recommend Spring AI to developers using Spring Boot and React, as well as those considering the introduction of AI systems in corporate environments. This course goes beyond a simple introduction to libraries; it helps you understand the core concepts and internal structure of Spring AI, and strengthens your practical implementation skills through various hands-on examples. The course is based on Spring AI version 1.1.x and provides continuous technical support and expansion strategies to ensure you can adapt to future version upgrades. Furthermore, we expand the practical value of Spring AI technology by utilizing MCP (Model Context Protocol) to achieve cross-domain context integration, AI linkage in distributed environments, and AI architecture design suitable for enterprise environments. With the goal of building AI systems that can actually be deployed in corporate settings, this will serve as a practical guide that balances theory and hands-on experience.

This is an advanced course focused on designing and implementing Multi-LLM architectures and orchestration-centered Agent systems (Main/Sub, Tool, Task Runtime, Agent Registry) by strategically combining GPT, Gemini, and LLaMA (local) based on Spring AI and Spring Boot. Moving beyond simple LLM calls, the curriculum covers the implementation of scalable, stable, and continuously improving AI systems. This involves applying Agentic Workflow Patterns (Chain, Parallel, Routing, Orchestrator–Workers, Evaluator–Optimizer) and Multi-Agent structures, separating execution layers like RAG and external APIs/DBs via Tool/ToolRegistry, and utilizing DAG engines, YAML declarative workflows, and Validated DSL (validation immediately after loading). Furthermore, the course includes Circuit Breakers, Reactive Streams, Redis monitoring, parallel processing, and iterative evaluation loops. It expands from Thymeleaf (SSR) exercises to decoupled Front-end/Back-end architectures using React and REST, while integrating tool and agent runtimes via MCP (Model Context Protocol) to build AI architecture design capabilities at a production-ready level. The ultimate goal is to evolve beyond being a simple AI user who merely integrates APIs and writes prompts, becoming a developer capable of designing AI systems—explaining and balancing Multi-LLM, agents, workflows, declarations, and validation within a single execution architecture.