LangChain on Azure - Building Scalable LLM Applications- (2026)

Mastering Model Context Protocol (MCP) is a practical, engineering-focused course designed to help developers build real, secure, and production-ready AI backends. After helping thousands of students overcome confusion around LLM integration, tool calling, and backend architecture, I created this course to solve the most common problems: “How do I build a reliable backend that LLMs can call safely?” “How do I choose between SSE, stdio, or streamable-http?” “How do I scale MCP into real applications with FastAPI, Auth0, and LangGraph?” “How do I structure my MCP tools, resources, prompts, and context?” In this course, I guide you step-by-step—from spinning up a minimal MCP server to deploying a fully secure, Dockerized system. Every lesson is hands-on, designed to remove complexity and give you a clear, repeatable workflow for building modern AI systems. If you're frustrated by vague tutorials and want a clear, concrete, engineering-level understanding of MCP, this course is built for you.

This course is designed to help absolute beginners learn Python through short, focused, and practical lessons. With daily bite-sized sessions of about 15 minutes, I guide students step by step through the core concepts of Python, making coding approachable and less overwhelming. Based on my experience helping beginners overcome common learning obstacles—such as confusion around syntax, logic, and problem-solving—I emphasize clarity, repetition, and hands-on practice. Each lesson builds on the previous one, reinforced by practical exercises and regular rest days for review and consolidation. By the end of the 30 days, students will not only understand Python fundamentals but will also confidently build their own command-line Python application, connecting all the concepts learned throughout the course.

In this course, you will learn how to design, build, and evaluate advanced RAG systems using the LangChain framework. You will master LCEL, Runnables, advanced retrieval techniques, chunking strategies, cross-encoder reranking, agent-based RAG, tool calling, SQL integration, and safety techniques using NeMo Guardrails. You will also learn how to trace, debug, and deploy a full-stack AI chatbot with LangFuse, React, FastAPI, and Docker.

What to Expect from This Course Welcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life. Course Highlights State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents. Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn. Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns. Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker. By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents. With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together!

This course proceeds with Python theory and practical sessions that even non-majors can easily understand. This is the first step in the overall roadmap built for application development - the Python programming introduction course. [Complete-Roadmap] Python Basics->Advanced Python->Automated Trading-Backtest Development->Automated Trading-Real-time Development->Automated Trading-LLM Application

Docker Compose always appears when you actually try to use Docker! You can learn what it is good for and how to use it through practice.

I've created a CI/CD introductory course that's easy to understand even for non-majors and can be applied immediately in real-world situations!

How did Assistant Manager Kim, who worked overtime every day, become a "leave-on-time pro" in just 1 hour? The secret to reclaiming your "clock-out time" Staying up all night because an Excel formula broke, or having an entire report ruined by a single typo—every office worker has experienced this at least once, right? This course was designed to put an end to that "recurring pain." You don't need to memorize complex coding syntax. We utilize Cursor AI, an AI-powered tool that writes code based on text commands, and Streamlit, which allows anyone to easily create websites. I have captured the entire process of solving the "pain points" I personally faced in the field—such as merging files by branch, automatically classifying items for repair, and generating infinite PDF certificates—all by "conversing" with AI without typing a single line of Python code yourself. By the end of this course, you will be reborn not as a mere office worker, but as a "Digital Transformation Expert" who revolutionizes your team's work efficiency.

Stop worrying about the dev team - I'll build it myself now! This course helps professionals in planning, marketing, sales, and other practical roles implement core web service features on their own using AI tools, without needing developer assistance.

This is not a lecture that simply memorizes commands. It's a practical introductory course that helps you intuitively understand the 'flow' of technology - why containers emerged, how Docker creates and runs images based on what principles. Even if you're encountering Docker for the first time, after taking this course, you'll be able to explain "why containers are a necessary technology" on your own.

This is a course to learn large-scale traffic processing and real-time streaming through practical projects by integrating RabbitMQ, Kafka, and Redis with Python Flask.

You will learn about domain purchase and deploying a Spring Boot production server using EKS, Docker basics, Kubernetes basics, AWS basics, CI/CD automation through GitHub Actions, ArgoCD, and general DevOps topics such as Prometheus/Grafana, etc.

This course conducts hands-on practice related to Diffusion models among generative artificial intelligence models. By reading and implementing the prompt-to-prompt paper, which is a representative Diffusion model application paper, we expect to cultivate the ability to understand the latest artificial intelligence papers.

You don't need to know any coding at all. In just half a day of studying VIBE Coding, you'll be able to create programs without writing any code. Let's learn the latest AI coding methods by directly creating 10 step-by-step examples such as web scraping, TTS, chatbots, and games.

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.

Stop using print()! Learn easy and powerful logging with Python's loguru library. From basic concepts to file management, FastAPI web app integration and Slack notifications, we'll teach you step by step with hands-on practice.

You will develop the ability to define problems based on data and clearly explain the rationale and decision-making process behind them. Additionally, rather than focusing solely on the performance of a single model, you will acquire a pipeline-oriented mindset that evaluates the completeness and reliability of the entire machine learning workflow. Furthermore, you will strengthen your problem-solving skills by tracing back the causes of errors when they occur and deriving improvement directions, and through end-to-end project experience, you will acquire practical ML pipeline capabilities that can be immediately applied in real-world settings.

Everything about Python asynchronous programming using FastAPI

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.

Develop a deep understanding and intuition for calculus. Solve problems and implement algorithms by hand and in Python.

A basic LangChain course that extracts only the essentials from the official documentation, all for the price of a cup of coffee(?). Get familiar with LangChain syntax before diving into full-scale development!

"Are you still studying with coding books?" A course, rated 5.0/5.0 by MBA and business undergrads at a London management university. You can take AI data analysis lectures from home. Even for humanities-background non-majors: practical data analysis skills immediately usable in the field, plus a data analysis portfolio for job search completion. I am a British Professor (British Professor) teaching data analysis and project classes with ChatGPT, no coding knowledge required.

Don't just remain a ChatGPT user; transform into a GPT service developer with Django/Channels. Quickly grasp the core of Django/Channels with this lecture!

The Channels library is a real-time messaging library developed under the philosophy of Django. Quickly learn the essence of Channels through this lecture.