Hands-on H&M Real-Time Personalized Recommender

Open-source course by Decoding ML in collaboration with Hopsworks.

🎯 About This Course

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:

• A practical 4-stage recommender architecture
• Two-tower model implementation and training
• Scalable ML system design principles
• MLOps best practices
• Real-time model deployment
• LLM-enhanced recommendations
• Building an interactive web interface

👥 Target Audience

This course is ideal for:

• ML/AI engineers interested in building production-ready recommender systems
• Data Engineers, Data Scientists, and Software Engineers wanting to understand the engineering behind recommenders

Note: This course focuses on engineering practices and end-to-end system implementation rather than theoretical model optimization or research.

🎓 Prerequisites

Category	Requirements
Skills	Basic knowledge of Python and Machine Learning
Hardware	Any modern laptop/workstation will do the job (no GPU or powerful computing power required).
Level	Intermediate

💰 Cost Structure

All tools used throughout the course will stick to their free tier, except OpenAI's API, as follows:

• Lessons 1-4: Completely free
• Lesson 5 (Optional): ~$1-2 for OpenAI API usage when building LLM-enhanced recommenders

📚 Course

This self-paced course consists of 5 comprehensive modules covering theory, system design, and hands-on implementation.

Our recommendation for each module:

1. Read the article
2. Run the Notebook (locally or on Colab)
3. Go deeper into the code

Module	Article	Description	Local Notebooks	Colab Notebooks
1	Building a TikTok-like recommender	Learn how to architect a recommender system using the 4-stage architecture and two-tower network.	No code	No code
2	Feature pipelines for TikTok-like recommenders	Learn how to build a scalable feature pipeline using a feature store.	•1_fp_computing_features.ipynb	-
3	Training pipelines for TikTok-like recommenders	Learn to train and evaluate the two-tower network and ranking model using MLOps best practices.	•2_tp_training_retrieval_model.ipynb •3_tp_training_ranking_model.ipynb	-
4	The inference pipelines	Learn how to deploy models for real-time inference (WIP)	•4_ip_computing_item_embeddings.ipynb •5_ip_creating_deployments.ipynb •6_scheduling_materialization_jobs.ipynb	-
5	Building personalized real-time recommenders with LLMs	Learn how to enhance recommendations with LLMs (WIP)	-	-

Note

Check the INSTALL_AND_USAGE doc for a step-by-step installation and usage guide.

🏗️ Project Structure

At Decoding ML we teach how to build production ML systems, thus the course follows the structure of a real-world Python project:

.
├── notebooks/          # Jupyter notebooks for each pipeline
├── recsys/             # Core recommender system package
│   ├── config.py       # Configuration and settings
│   ...
│   └── training/       # Training pipelines code
├── tools/              # Utility scripts
├── streamlit_app.py    # Streamlit app entry point
├── .env.example        # Example environment variables template
├── Makefile            # Commands to install and run the project
├── pyproject.toml      # Project dependencies

🌐 Live Demo

Try out our deployed H&M real-time personalized recommender: 💻 Live Streamlit Demo

Important

If you get ModelServingException or ConnectionError errors, the instances are still scaled to 0, so give it a few minutes to scale up. Then, refresh the page. This happens because we are in demo, 0-cost mode:

• Scaling from 0 to +1 instances may take 1-2 minutes.
• If you encounter connection errors, try selecting different customers or refresh the page.
• The system will become responsive once the deployment is active.

🚀 Getting Started

For detailed installation and usage instructions, see our INSTALL_AND_USAGE guide.

Recommendation: While you can follow the installation guide directly, we strongly recommend reading the accompanying articles to gain a complete understanding of the recommender system.

💡 Questions and Troubleshooting

Have questions or running into issues? We're here to help!

Open a GitHub issue for:

• Questions about the course material
• Technical troubleshooting
• Clarification on concepts

When having issues with Hopsworks Serverless, the best place to ask questions is on Hopsworks's Slack, where their engineers can help you directly.

Sponsors

Hopsworks

Contributors

Paul Iusztin AI/ML Engineer

Anca Ioana Muscalagiu AI/ML Engineer

Paolo Perrone AI/ML Engineer

Hopsworks's Engineering Team AI Lakehouse

License

This course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge your project is based on our work, you can safely clone or fork this project and use it as a source of inspiration for your educational projects (e.g., university, college degree, personal projects, etc.).