Docker virtualization technology for DevOps (Private Harbor Registry)

Hello. We’ve added a new supplemental lecture to the Practical LLM Service Development for Spring Developers (Part 1) course. In this approximately one-hour lecture, you’ll learn how to integrate TypeSafe AI’s recently released decision model, Jev, into a Spring Boot application.

Why was it added?
In Section 4, we covered how to use entity() to receive an LLM response as a Java object, as well as the practical pitfall of parsing breaking when the model wraps JSON in code fences. Whether you ask through a prompt or automate it with entity(), the limitation remains that you are ultimately "asking" the LLM to follow a particular format.
Jev takes a different approach. Instead of writing text, it chooses an answer from a predefined set of options and returns it along with its probability. In other words, the format is structurally guaranteed. Processing the same input in both ways makes it much clearer what to consider when designing "structured output." That is why we organized this content as an extension of Section 4.

What will you learn?

1. The Concept of Jev and System One Models: What It Means to Return a “Typed Decision” Instead of Text

2. The difference between LLMs and Jev: descriptive answers vs. multiple-choice answers, and what each does well and poorly

3. Three question types (choice · score · noul) and the /v1/systemone request structure

4. Calling from Spring: Call the Jev API with RestClient and use a Java enum to create choices and convert the response in a type-safe manner.

5. Comparison demonstration: Analyze the same movie review using entity() and Jev, then compare speed, format errors, confidence, and consistency.

Updated materials included

• Section 4 Example Code (ch4): Two Review Analysis APIs (/api/movies/review/llm, /api/movies/review/jev) and the Addition of JevClient

• Supplementary lecture slides and course materials

• Comparison test scripts: review-compare.sh for macOS and review-compare.ps1 for Windows

Things to know in advance

• Jev is not a feature of Spring AI, but rather a separate external service provided by TypeSafe AI. In the course, it is called directly using Spring AI's RestClient, not its ChatClient.

• Jev is currently in the early access stage, so there may be a wait to obtain an API key. Jev is disabled by default in the example code (jev.enabled=false), so you can proceed with the existing exercises as usual even without a key. If you don't have a key, watch the demonstration in the video and follow along with a focus on the concepts and code.

• As this service has just been launched, its pricing or API may change later. If any changes occur, we will notify you again through a new announcement.

📌 Go directly to the course: https://inf.run/VVpnV

If you have any questions while taking the newly added lessons or get stuck during the hands-on exercises, feel free to leave a comment. We’ll continue to regularly add content that you can use right away in your work.

Hello, I’m Lee Do-won.

"I’ve created Jenkins pipelines, but I’ve only heard of ArgoCD and GitOps as concepts."

"I’m at a loss as to how to design deployments for each dev, staging, and production environment."

For those facing these challenges, we have opened the “Practical GitOps CI/CD with Jenkins and ArgoCD” course on Inflearn.

Here’s what sets this course apart

• Beyond traditional Jenkins-based CI/CD, we also cover ArgoCD-based GitOps.

• We directly compare the Push (Jenkins) and Pull (ArgoCD) approaches on the same cluster. When someone makes a manual change with kubectl, you can see firsthand how one remains unchanged while the other automatically recovers, and experience the differences in how rollbacks and deployment histories are reviewed.

• Combine the two approaches to build a hybrid deployment pipeline from start to finish, with Jenkins Push for dev·staging and ArgoCD Pull (manual Sync approval) for production.

• You will practice essential deployment techniques needed for operations, including canary deployments, blue-green deployments, RBAC, approval procedures, incident response, and automatic rollbacks.

Practice Environment

We’ll proceed using just Docker Desktop. It works on both Windows and macOS, and you don’t need a cloud account such as AWS or any additional fees.

Recommended for

• Backend and infrastructure engineers who have used Jenkins but are new to ArgoCD/GitOps

• Those who want to move Kubernetes deployments from manual kubectl apply to automation

• DevOps and SRE professionals concerned with operational requirements such as deployment approvals, rollbacks, and audit trails

The total runtime is approximately 10–11 hours, and hands-on materials and a troubleshooting guide are also included.

👉 View the course: https://inf.run/dEjZE

If you have any questions, feel free to leave them in the comments or in the course Q&A!

We have newly launched the Practical LLM Service Development for Spring Developers (Part 1) course. Although it may feel a little overdue, this course covers what you need to learn to develop LLM applications using the Spring framework, which is familiar to Java developers. We hope you will take a great interest in it.

Why did I create this course?

These days, keywords like RAG, vector search, and AI agents can be found everywhere, but there aren’t many resources that address, at a practical level, “how to integrate them with a Spring Boot backend.”

This course incorporates into its curriculum the problems I encountered while debugging an actual RAG recommendation endpoint—including how chunking documents by token broke their semantic units and degraded search quality, and how I solved the problem by switching to paragraph-based chunking. Rather than simply showing you the correct code like a tutorial, we’ll follow along together to understand “why it doesn’t work → how to diagnose it → how to fix it.”

Recommended for the following people

• Backend developers with Spring Boot experience who are new to developing LLM/AI services

• Developers who want to apply RAG and vector search at the level of real-world projects, not just tutorials

• Professionals who need to directly implement internal AI features (chatbots, document search, recommendations, etc.) using Spring.

Experience creating REST APIs with Spring Boot is all you need. No prior knowledge of AI/LLMs is required.

What will you learn?

1. Spring AI Core Fundamentals Basic usage of ChatClient, structured output, and streaming response implementation

2. Building a RAG Pipeline Vector store integration, comparison of document chunking strategies and hands-on troubleshooting, search quality improvement, and prompt design

3. Tool and Multimodal Expansion Integrating external tools using MCP (Model Context Protocol), processing multimodal inputs such as images, and integrating with Dify

4. Service Stabilization and Real-World Projects Applying guardrails, designing reliable services, and a mini project that integrates everything you have learned so far

The appendix also covers how to use AI coding tools and Ollama.

Series Guide

This release is Part 1 of the full series. Parts 2 and 3 will cover more advanced architectures and real-world cases, and once the entire series is released, we plan to offer a discount on the full package.

📌 Go to the course: https://inf.run/VVpnV

Feel free to leave any questions or inquiries about the curriculum in the comments. Through Part 1, I hope to take the first step with you toward integrating an LLM into your Spring service.

Hello, this is Lee Do-won.

In the lectures "Building a CI/CD Pipeline Using Jenkins" and "Docker Virtualization Technology for DevOps (Private Harbor Registry)", examples are run by building a practice environment using the DinD (Docker in Docker) method to ensure smooth execution of Docker containers.

However, the Windows and MacOS environments are different, and in the case of MacOS, the Docker image that needs to be configured is different depending on the Intel chip and Apple chip. The image is configured to have the SSH server and Docker engine installed, so that it is easy to use when multiple Docker containers are needed during practice, but it is true that it is difficult to produce it to fit all the students' environments. However, we are trying to update the Docker image so that practice can be done without difficulty in the most general environment.

Recently, when practicing using the docker-server:m1 image used on MacOS Apple chips, errors such as the following were found that prevented practice.

 exec /docker-entrypoint.sh: invalid argument

I checked and fixed it through various routes, but it seems that the error occurred in the DinD method that installs the Docker engine in the Docker container and uses it on the arm64 architecture used in MacOS Apple chips. (It is difficult to find the cause because there is no error message or log.)

• Example error: https://www.inflearn.com/community/questions/1401289/service-create-%EC%8B%9C%EC%97%90-%EC%97%90%EB%9F%AC-%EB%B0%9C%EC%83%9D

As it was determined that it was difficult to resolve with the image in question, we rebuilt and distributed a version that installed the SSH + Docker engine on the Ubuntu base as shown below.

https://hub.docker.com/repository/docker/edowon0623/docker-server/general

We have confirmed that all the problems that occurred above have been resolved. In Ubuntu, to start the Docker service, first run the command below and then practice.

 service start docker

We will continue to provide an updated practice environment as issues related to practice, such as DinD problems, are identified.

Next week is a holiday. 2024 was a year of many hardships for me personally, but I will start the new year with a new mindset. I will visit you often with better lectures this year. Happy New Year .

thank you