Learning path

Production GenAI Engineer Learning Path

A structured roadmap for taking GenAI apps to production: MCP servers and clients, agent projects with FastAPI and AWS, production RAG, and fine-tuning LLMs with Hugging Face.

Start at Stage 01 See full curriculum

4
Structured courses: 200,000+
Students enrolled: Self-paced
On demand

The route01MCP Mastery02Production ML03Production ML04Fine-Tuning

Outcomes

Skills You'll Master

Structured Modules

Step-by-step sequential learning stages.

Expert Guidance

Master complex topics with detailed code walkthroughs.

Hands-on Projects

Build portfolio-ready applications at each stage.

Production Ready

Learn industry-standard deployment and best practices.

Why the sequence matters

Who This Path Is For

The difference? A learning path that starts where most tutorials stop, at shipping:

MCP integration -> Agent projects and deployment -> Production RAG -> Fine-tuning your own models

This roadmap is for developers who can already build a chatbot or a simple agent and now want to put real GenAI applications in front of users.

A demo that works on your laptop is the easy part. A production GenAI app also needs an API, a user interface, retrieval that holds up on messy documents, a clean way to connect tools and data, and sometimes a model tuned to your own domain. This path covers each of those in turn, one course per stage.

The progression

01MCP IntegrationFoundation

02Agent Projects and DeploymentBeginner

03Production RAGAdvanced

04Fine-Tuning LLMsExpert

4 stages

Pathway Curriculum

Each stage pairs the concepts you need with the course that teaches them.

Stage 01

MCP Integration

Start with the layer every production AI app needs: a clean way to reach tools and data. Without it, every new tool or data source needs its own glue code. The Model Context Protocol (MCP) gives AI apps one standard way to do this. In this stage you build MCP servers and clients in Python, connect them to Claude Desktop, and use them inside LangChain and LangGraph agents.

Topics you will master

MCP architecture: client, server and transport layers
Connecting MCP servers directly to Claude Desktop
Data analysis servers for Excel, PowerPoint and SQLite
RAG with vector databases through LangChain
Testing, security and cloud deployment of MCP servers

Best forDevelopers who want their AI apps to use tools and data in a reusable way.

Expected outcomeYour own MCP servers and clients, plugged into agents and desktop AI apps.

Stage 02

Agent Projects and Deployment

Next, build complete AI agents and ship them, using MCP where it fits. This stage covers how an agent reasons and calls tools, how it remembers past turns, and how to keep it safe with guardrails and human approval. You then wrap the agent in a FastAPI service with streaming responses, connect a Streamlit front end, and deploy the whole app on AWS EC2.

Topics you will master

Agent architecture: ReAct reasoning, tool calling and structured decisions
Short-term and long-term memory using databases and embeddings
Guardrails: human-in-the-loop, middleware controls and sandboxed code execution
FastAPI REST endpoints with validation, CORS and SSE streaming
Deploying AI agents on AWS EC2 with MCP integration

Best forDevelopers who have built a basic agent and want to turn it into a deployed application.

Expected outcomeWorking AI agents served through an API and a UI, running in the cloud.

Stage 03

Production RAG

Most GenAI apps answer questions from documents, and most RAG demos break on real data. In this stage you build RAGWire, a production-grade RAG toolkit with LangChain, Qdrant and LangGraph. You combine keyword and vector search, add agents that grade their own retrieval and rewrite weak queries, and deploy the result to several cloud platforms.

Topics you will master

Hybrid retrieval: BM25 sparse and dense search with Reciprocal Rank Fusion
Multiple LLM providers: OpenAI, Groq, Gemini, Ollama and Hugging Face embeddings
Agentic RAG that grades retrieval quality and rewrites queries
Multi-agent systems with CrewAI, Microsoft AutoGen and LangGraph routing
Chainlit chat UI with auth, and OpenAI-compatible FastAPI endpoints
Deployment on Render, Railway, AWS ECS Fargate, GCP Cloud Run and Azure

Best forDevelopers whose RAG prototype needs to hold up in production.

Expected outcomeA production RAG system with hybrid search, an API, a chat UI and cloud deployment.

Stage 04Final stage

Fine-Tuning LLMs

Prompts and retrieval go a long way, but some tasks need a model trained on your own data. This last stage explains how the Transformer architecture works, then walks through preparing a custom dataset, fine-tuning a model with Hugging Face, and evaluating the result.

Topics you will master

Transformer architecture fundamentals and the math behind them
Preparing and formatting custom datasets for training
Fine-tuning strategies and training optimization
Evaluating and tuning model performance

Best forDevelopers who need a model adapted to their own domain or task.

Expected outcomeFine-tuned LLMs trained and evaluated on your own data.

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