Stop building chatbots. Start engineering agents.

This course is your end-to-end blueprint for designing, testing, and deploying autonomous agents that drive real-world workflows. You'll master the full AI engineering lifecycle and ship a capable agent that doesn’t just answer questions, but gets the work done.

How do AI agents really work behind the scenes? This course breaks it all down.

We’ll teach you how to implement planning loops, manage context, give agents memory, and handle multimodal data.

It's a deep dive into the fundamentals of agentic design.

Ever dreamed of building your own AI game?

This course teaches you to design and implement a production-ready RAG and LLM system from the ground up. By building an AI simulation engine, you'll gain hands-on experience with LangGraph, FastAPI, Docker, and the LLMOps practices needed to ship real-world AI products.

Build a fully functional AI Pull Request Reviewer from scratch. This project-based series guides you through using the Model Context Protocol (MCP) to connect GitHub, Asana, and Slack into a single, automated workflow.

You'll learn to orchestrate a complex system and walk away with a powerful, real-world example of enterprise AI automation.

Master the end-to-end process of building production-ready recommender systems.

This hands-on course uses a real-world H&M dataset to teach you how to architect an ML system, train neural network models, and apply LLM techniques for personalization.

Finally, you'll deploy your system on Kubernetes using KServe and MLOps best practices.

Transform your knowledge base‍ → your notes, resources, and ideas into an intelligent AI assistant you can chat with.

This course guides you through 6 modules on building an advanced, production-ready agentic RAG system, applying modern LLMOps and ML best practices along the way.