Production AI Systems with LangChain & LangGraph

Build production AI apps with RAG, LangSmith, FastAPI, Docker, Security, Testing, and Enterprise Deployment

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

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-Grade AI Systems — Not Toy Chatbots
Most AI courses focus on prompts, simple chatbots, and isolated demos. Real-world AI engineering requires much more.

In this course, you'll learn how professional teams design, build, test, secure, observe, and deploy production AI systems using the modern LangChain ecosystem.

Starting from the fundamentals of LLM architecture and prompt engineering, you'll progressively build advanced AI applications using LangChain, LangGraph, LangSmith, FastAPI, vector databases, multimodal pipelines, and enterprise deployment workflows.

You'll learn how to build Retrieval-Augmented Generation (RAG) systems, create stateful LangGraph workflows, instrument applications with LangSmith observability, secure APIs with JWT authentication, deploy containerized AI services with Docker, and validate reliability through AI Quality Engineering practices.

Unlike beginner tutorials, this course emphasizes maintainable architectures, observability, testing, governance, security, scalability, and real-world deployment patterns used in modern enterprise environments.

What You'll Build

• Production-grade RAG systems
• 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
• Complete capstone AI application

Technologies Covered

• Python
• LangChain
• LangGraph
• LangSmith
• FastAPI
• OpenAI APIs
• ChromaDB
• Vector Databases
• Docker & Docker Compose
• Redis
• JWT Authentication
• Ollama
• Whisper
• Pytest
• Async Python

By the end of this course, you'll have the skills to architect, develop, test, secure, and deploy production-ready AI systems with confidence.

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

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