Master Production AI with Our End-to-End Open-Source Courses
Experience real-world projects that teach production AI, covering everything from ML system architecture to data collection and serving models.
Our mission is to teach you how to design, code, and deploy production-grade AI, GenAI, and information retrieval systems—built with ML systems, MLE, and MLOps best practices in mind.
Driven by our passion, we've put together a series of open-source courses that are entirely free of charge!
You can find them on our GitHub and Substack. Each course includes carefully written code and engaging written lessons designed to make your learning experience enjoyable and effective.
As you may notice, our courses are more than just notebooks that show you how to use a framework or algorithm. Along with using AI models and frameworks, we will show you how to:
Build professional Python applications.
Architect AI systems using MLOps/LLMOps best practices.
Write clean, modular, and scalable Python code using SWE best practices.
Preprocess custom datasets instead of using standard, static datasets.
Integrate AI models into real-world projects and applications.
No hidden costs, no registration required—just clone one of our repositories, open our Substack, and you’re good to go! It’s all open-source and completely self-paced, so you can jump in whenever you’re ready.
How can you participate in our courses?
Navigate to one of our GitHub repositories and clone it.
Open the Substack lessons found in the repository’s GitHub docs.
Set up the code using the documentation from the repository.
Start going through the lessons and running the code.
The best part? We encourage you to reuse our code for your open-source projects! If you do, DM us on Substack, and we’ll share your project on our socials.
Enjoy!
Available courses:
Hands-on Amazon Tabular Semantic Search
Tutorial on building a modern search app for Amazon e-commerce products leveraging tabular semantic search and natural language queries.
Perfect for developers building search functionality in e-commerce or structured data applications.
Hands-on H&M Real-Time Personalized Recommender
This hands-on course teaches you how to build and deploy a real-time personalized recommender system for H&M fashion articles. You'll learn:
To architect a modern ML system for real-time personalized recommenders.
To do feature engineering using modern tools such as Polars.
To design and train ML models for recommender systems powered by neural networks.
To use MLOps best practices by leveraging Hopsworks AI Lakehouse.
To deploy the recommender on a Kubernetes cluster managed by Hopsworks Serverless using KServe.
To apply LLM techniques for personalized recommendations.
LLM Twin Course: Building Your Production-Ready AI Replica
By finishing the "LLM Twin: Building Your Production-Ready AI Replica" free course, you will learn how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices.
No more isolated scripts or Notebooks! Learn production ML by building and deploying an end-to-end production-grade LLM system.
Second Brain AI Assistant Course
The Second Brain AI Assistant course contains 6 modules that will teach you how to build an advanced 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 personal 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.
