Master Production AI with End-to-End, Open-Source Courses.
Write real code. Ship real AI applications.
Our free, end-to-end courses teach you how to design, code, and deploy production-grade AI, GenAI, and information-retrieval systems—grounded in real-world ML, MLE, and MLOps best practices.
These aren’t just notebooks or framework demos. Each course combines clean, reusable code with clear written lessons, covering everything from system architecture and data pipelines to model serving and deployment.
What you’ll learn.
— Build professional, production-ready Python applications.
— Architect AI systems using MLOps & LLMOps best practices.
— Write clean, modular, and scalable Python code using SWE best practices.
— Work with real, custom datasets.
— Integrate AI models into real-world projects & applications.
How to get started.
1. Choose one of the courses below.
2. Clone one of our GitHub repositories.
3. Open the linked Substack lessons.
4. Set up the code using the documentation from the repository.
5. Start going through the lessons and running the code.
PhiloAgents Course
Welcome to PhiloAgents, a team-up between Decoding AI and The Neural Maze, where ancient philosophy meets modern AI.
In this hands-on course, you'll build an AI agent simulation engine that brings historical philosophers to life in an interactive game environment.
Designing Enterprise MCP Systems
The Enterprise MCP Series is an open-source initiative designed to help you build modular, production-grade AI automation systems using the Model Context Protocol (MCP).
Unlike simple tutorials, this series tackles real-world, enterprise use cases—like a Pull Request Reviewer Assistant that analyzes GitHub PRs, pulls context from GitHub and Asana, and posts actionable insights to your team on Slack.
Hands-on H&M Real-Time Personalized Recommender
The Hands-on H&M Real-Time Personalized Recommender, in collaboration with Hopsworks, is a 5-module course backed up by code, Notebooks and lessons that will teach you how to build an H&M real-time personalized recommender from scratch.
By the end of this course, you will know how to architect, build and deploy a modern recommender.
Second Brain AI Assistant
The Second Brain AI Assistant course contains 6 modules that will teach you how to build an advanced agentic RAG and LLM system using LLMOps and ML systems best practices. You'll learn to build an end-to-end AI assistant that chats with your Second Brain - your knowledge base of notes, resources, and storage.
By the end of this course, you'll be able to architect and implement a production-ready agentic RAG and LLM system from scratch.