Welcome to Free download educational resource and Apps from TUTBB

TUTBB · 2026-06-08T02:29:25+0100

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
Homepage

Code:

https://www.udemy.com/course/production-ai-systems-with-langchain-langgraph

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Welcome to Free download educational resource and Apps from TUTBB

Production AI Systems with LangChain & LangGraph

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