Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
LangChain & LangGraph Masterclass: Build Production AI App
21 students

LangChain & LangGraph Masterclass: Build Production AI App

Build Real-World RAG, AI Agents, FastAPI APIs, Vector Databases, LangSmith & Docker-Based AI Systems
Created bySudip Bhattacharyya
Last updated 7/2026
English
English [Auto],

What you'll learn

  • Build production-grade AI applications using LangChain, LangGraph, and modern LLM architectures
  • Design and implement enterprise Retrieval-Augmented Generation (RAG) systems with vector databases
  • Create stateful AI workflows, multi-step orchestration pipelines, and autonomous agent systems
  • Build secure FastAPI backends with authentication, streaming responses, and production APIs
  • Implement observability, tracing, evaluation, and debugging using LangSmith
  • Develop multimodal AI applications that process text, images, PDFs, and audio
  • Deploy scalable AI systems using Docker, cloud infrastructure, caching, and production engineering practices
  • Build and deploy a complete enterprise AI application from architecture and development to testing and deployment

Course content

13 sections • 16 lectures • 13h 18m total length

  • Introduction11:29

Requirements

  • Intermediate Python programming knowledge
  • Basic understanding of APIs, HTTP requests, and backend development concepts
  • Familiarity with Git and command-line tools
  • A computer running Windows, macOS, or Linux with Python 3.11+ installed
  • An OpenAI API key for hands-on exercises
  • No prior experience with LangChain, LangGraph, or AI frameworks is required

Description

Build Production-Ready AI Applications for Real-World Software Development

Most AI courses teach prompts, simple chatbots, or isolated code examples. Real-world AI applications require much more than calling an LLM API.

In this course, you'll learn how to design, build, test, secure, monitor, and deploy production-ready AI applications using the modern LangChain ecosystem and industry best practices.

Starting with the fundamentals of Large Language Models (LLMs), prompt engineering, and AI application architecture, you'll progressively build real-world projects using LangChain, LangGraph, LangSmith, FastAPI, vector databases, Docker, and modern AI engineering techniques.

By the end of this course, you'll understand not only how AI applications work, but also how professional engineering teams build systems that are scalable, reliable, secure, and ready for production.

What You'll Learn

You'll learn how to:

  • Build Retrieval-Augmented Generation (RAG) applications

  • Create stateful AI agents with LangGraph

  • Develop production-ready AI APIs using FastAPI

  • Add observability and tracing with LangSmith

  • Integrate vector databases for semantic search

  • Build streaming AI applications

  • Secure AI services using JWT authentication

  • Deploy containerized AI applications with Docker

  • Test and validate AI systems using modern AI quality engineering practices

  • Design maintainable, scalable AI architectures for real-world deployment

Hands-On Projects

Throughout the course, you'll build practical projects including:

  • Production-grade RAG applications

  • LangGraph orchestration workflows

  • Multi-step AI pipelines

  • FastAPI AI backends

  • Streaming AI applications

  • JWT-secured AI services

  • LangSmith observability pipelines

  • Multimodal document intelligence systems

  • Enterprise AI architecture projects

  • A complete production-ready capstone application

Technologies Covered

  • Python

  • LangChain

  • LangGraph

  • LangSmith

  • FastAPI

  • OpenAI APIs

  • ChromaDB

  • Vector Databases

  • Docker & Docker Compose

  • Redis

  • JWT Authentication

  • Ollama

  • Whisper

  • Async Python

  • Pytest

Who This Course Is For

This course is ideal for:

  • Python developers who want to build AI applications

  • Software engineers moving into AI engineering

  • Backend developers interested in production AI systems

  • AI developers who want to move beyond simple chatbot tutorials

  • Anyone who wants to understand how enterprise AI applications are designed and deployed

Why This Course Is Different

Rather than focusing on isolated demos, this course emphasizes production AI engineering.

You'll learn how to build AI systems that are:

  • Maintainable

  • Scalable

  • Secure

  • Observable

  • Testable

  • Ready for real-world deployment

These are the same engineering principles used by professional software teams when developing modern AI applications.

By the end of this course, you'll have the confidence to architect, build, test, secure, and deploy production-ready AI applications using today's most important AI engineering tools and frameworks.

Who this course is for:

  • Python developers who want to transition into AI Engineering
  • Backend engineers building production AI and LLM-powered applications
  • Software architects designing enterprise AI platforms and intelligent systems
  • AI and ML engineers looking to learn modern production deployment practices
  • Technical founders and consultants building AI products and client solutions
  • Developers who want hands-on experience with LangChain, LangGraph, RAG, and LangSmith
  • Engineers interested in enterprise-grade AI architecture, security, observability, and deployment

Report abuse