MLflow with Silicon Valley Engineers

Are you using AI but still typing new prompts every single time? Code is being generated, but are team-level automation and systematization still out of reach? This course is not about learning Claude Code as a "conversation tool." It is a course where you will learn how to design AI as a system using Agents, Sub-agents, Agent Teams, Memory, and Hooks.

Uncover true value in the age of data! 📊 Designing applications focused on data has now become essential. Develop the insight and skills your company wants with the latest trends and practical case studies. Start now with efficient data processing and design secrets! Your next step, leap into the data-driven world!

"The technology that prepares you for the future, Docker" This course focuses on learning Docker quickly and easily. It explains the basic concepts and usage of Docker in an intuitive and easy-to-understand way, and through interesting practical exercises, you can learn solid skills that can be used immediately in practice, such as creating containers, building images, and setting up networks. This is the perfect course for those who want to quickly absorb and apply the latest technology to their work!

Now, your AI is no longer in the cloud. It runs directly inside your laptop. Using LM Studio and Ollama, we will show you how to build a Private AI environment that runs entirely locally— from data processing and document analysis to code generation. Without security concerns, without costs, and even faster— Move beyond simply "using" AI and start "truly mastering" it.

Learn Everything About Python from a Silicon Valley Software Engineer. Currently, as a 15-year veteran software developer, I handle everything from web applications, big data, and even SRE & DevOps, all with Python. From the basics of Python to advanced techniques, don't miss this opportunity to learn all the skills and know-how for using Python in real-world Silicon Valley practice!

Gradle Masterclass for Efficient Build Management – Learn how to automate complex builds and increase project productivity. This course covers the core concepts and practical application of Gradle in detail. You will be able to build a professional-level build environment in no time.

From a developer who only used ChatGPT to a developer who handles AI agents. Learn practical ways to maximize coding productivity using Codex's Rules, Hooks, Skills, and MCP.

This course teaches you how to build AI agents from scratch to a production-ready level using OpenClaw. Beyond a simple introduction to features, you will master OpenAI, local LLMs, memory, sessions, Tools, and Skills in a single, cohesive workflow. Based on hands-on experience and know-how gained from developing AI and large-scale systems in Silicon Valley, the course explains architectures and operational methods applicable to real-world projects. By the end of the course, you will be able to design your own AI agents and implement automation by integrating them with various services. This is a practice-oriented course for those who want to go beyond just using AI and learn how to build and operate it themselves.

Learn how modern data teams leverage dbt. Build maintainable analytics models and validated data pipelines hands-on. Master core concepts of analytics engineering with a practical focus. This course is for those who want to take their data career to the next level 🚀

Apache Superset is an open-source BI tool that lets you quickly create charts and dashboards by connecting multiple databases. You can visualize data without knowing code, and SQL Lab allows you to run queries directly, making it useful for everyone from beginners to professionals. The dashboards you create can be shared immediately with permission settings, making it perfect for collaboration. Learning Superset gives you the ability to make data-driven decisions quickly and build practical dashboards you can use right away at work. In short, it's perfect for anyone who wants to make data "speak." 🚀

🔥 Vibe Coding with Streamlit 🔥 Real-time coding experience where you can instantly create your own web service with just one line of code! Visual-focused, hands-on lectures tailored to MZ sensibilities - no complex backend knowledge required. Learn the basics of Streamlit and awaken your development instincts by directly implementing practical apps. Complete your own "personal service" with code in a vibe way right now 😎💻

Real-time data processing is now essential, not optional! Learn to handle real-time streaming smartly with Apache Flink. Batch? Streaming? Even complex concepts can be understood easily and quickly. Hands-on practice configuration where you'll get the hang of it by directly working with Kafka and DB integration! In a world driven by data, start first with Flink.

“There are many ways to solve problems, but why do we feel lost during interviews?” This lecture offers coding interview survival strategies directly from a current engineer working in Silicon Valley. Focusing on problems frequently encountered in real interviews, we'll guide you not just through simple solutions, but also how to articulate your thought process and how you'll be perceived from the interviewer's perspective. Designed for mid-level developers and above, the curriculum is optimized for real-world interviews. If you are preparing for coding tests and technical interviews, gain practical skills and confidence through this lecture.

This is a practical, hands-on course designed so that even SQL beginners can start easily and apply it right away. ✨ Learn to pull data with SELECT, connect with JOIN, organize with GROUP BY, and more— master only the most frequently used features in real work, in an easy and fun way! 📊 We minimize complex explanations and focus on realistic examples that make you think, "Ah, this is why SQL is important!" 💡 Data analyst, developer, data engineer… Whatever path you choose, SQL is an essential skill. 🚀 With this one course, solidify your fundamentals and develop the data intuition to solve problems on your own in real-world situations! 🔥 Start right now. This is the easiest first step into the world of data! 🌍💙

Master AWS networking completely! Learn VPC, subnet, routing, and security with a focus on practical application, and design secure and scalable cloud network architectures. Complete the core skills that every working engineer must know in a short period of time!

The essential technology of the cloud era, the complete guide to Amazon EKS! Manage Kubernetes clusters easily and quickly with EKS, from container orchestration to practical application.

Learn Elasticsearch, the core technology for data search and analysis in the rapidly growing IT field! We will teach you how to solve complex searches simply, along with hands-on practice that you can apply right away. Are you ready to upgrade your digital skills?

Linux is an operating system widely used on various platforms worldwide, including servers, the cloud, and IoT devices. Through this course, you can enhance your competitiveness in the IT field based on a deep understanding of Linux. Furthermore, by acquiring expertise in open-source technology, you can prepare for the future technological landscape and stand out in a high-demand job market.

Redis is a fast and scalable open-source database. This technology is used to maximize performance in real-time applications, games, social media platforms, and more. However, to truly understand the power of Redis and use it effectively, you need a deep understanding. Now is your chance to learn Redis in a professional way. "Redis with Silicon Valley Engineers" covers a wide range of topics, from the core concepts of Redis to advanced features. This course is designed for everyone, from beginners who are new to Redis to advanced users.

Master asynchronous tasks and distributed systems with Python Celery! Through a combination of theory and practice, you can learn essential skills and optimization strategies necessary for everyone from beginners to experts. The course covers a wide range of topics, from Celery installation, configuration, and task definition to monitoring, and is designed to help participants become proficient in Python-based asynchronous task management.

The digital transformation (DT) and introduction of artificial intelligence (AI) in companies begin with the construction of machine learning models. However, the scope of machine learning technology is very broad, and in order to select the optimal method, it is necessary to clearly understand the basic concepts. In this lecture, we will introduce the core contents necessary to clearly understand the basic concepts of machine learning, focusing on five examples.

This is the second lecture for popularizing Java machine learning. We introduce Weka, which provides UI and API so that both design and coding can be implemented. We have included cases that are completely suitable for practical application in the lecture.

See exam types and trends at a glance! Strengthen your practical skills with approximately 100 questions. The perfect preparation for the AWS AI Practitioner exam, allowing for systematic study even when preparing on your own.

When you learn machine learning, you learn how to improve accuracy. But the moment you try to put that model onto an actual chip, questions no one has answered remain. How many multiplications does this model perform? How much does it need to remember, and for how long? Can the chip specifications you are targeting handle those requirements? Machine learning textbooks talk about accuracy, and semiconductor textbooks talk about circuits, but materials that bridge the gap between them are rare. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs. The model with the highest accuracy often turned out not to fit on the board. In this course, you will build six algorithms directly in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy each time. Then you will convert those figures into the resources of an actual FPGA board. Saying that something requires a certain number of multiplications is not enough to gauge its size, but knowing that it uses a certain percentage of the board makes the decision clearer. After nine Labs, when the six models are brought together in a single table, an unexpected picture emerges. We spent nine lessons counting multiplications, but multiplications were not what held us back. After that, we reduce floating-point values to integers, manage bit widths, and even establish criteria for evaluating the circuit. You do not need an FPGA board. The course is conducted entirely in Python, and the board is used only as a reference for converting the measurements.

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

This course aims to cover the latest Gemini API SDK (@google/genai) in a Node.js and TypeScript environment. You will learn how to use multimodal technology to process text, images, and video simultaneously. We will practice the entire process, from issuing an API key to project setup and model calling.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

We provide guidance on what to study for those starting AI development.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

You will learn about Transformers inside out by directly implementing everything from Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder-based MarianMT translation model, and the Vision Transformer in code.