MLflow with Silicon Valley Engineers

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!

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.

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.

"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!

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!

This is a practical Ansible course taught by Silicon Valley engineers. Ansible is a powerful tool for automated infrastructure management and provisioning, saving time and effort and building a consistent environment.

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

How to Read the World with Data: Explore Your Own Data Science! Dive deep and practically into the tools and techniques that form the core of modern data science. In particular, you will learn how to analyze data and implement algorithms using Anaconda, Numpy, Pandas, and Scikit-learn, essential components of data science.

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.

“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.

"Enter the world of Cloud Infrastructure Automation!" Terraform, an infrastructure automation tool, is used to provision and manage resources in a cloud environment. Now, you too can define and automate your cloud infrastructure as code to quickly and consistently configure an infrastructure environment!

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.

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.

Understand the operating principles of generative AI models using deep learning and acquire practical application skills through hands-on exercises.

Learn MLP, CNN, and RNN, the basic framework of deep learning, quickly and easily.

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

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.

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.

This course is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

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."

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.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

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.

In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.

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.

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

This course covers the entire process of evolving ChatGPT from a simple conversational AI into an intelligent AI Agent that performs real-world tasks. By utilizing the three core frameworks—LangChain, LangGraph, and LangSmith—you will build AI Agents that can be used immediately in practice.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

This course is designed to learn the fundamental theories and practical processes necessary for applying AI modeling and service planning to real-world work environments. Learners can acquire essential foundational knowledge to skillfully perform AI modeling projects and experience various practical procedures used in actual business settings. The course is specifically structured to provide step-by-step learning of the entire workflow from AI service environment analysis → goal setting → requirements analysis → service model design → scenario planning → utilization planning → execution plan development → performance evaluation planning. Through this approach, learners can develop systematic thinking and practical application skills for how to introduce and utilize AI from business and service planning perspectives, rather than limiting themselves to purely technical learning. Upon completing this course, learners will understand the overall planning and execution process of AI projects and will be able to strengthen their capabilities as AI service planners, PMs, and data-driven strategists in actual corporate environments.