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.

What you'll do:

1. Architect a scalable and modular ML system using the Feature/Training/Inference (FTI) architecture.

2. Feature engineering on top of our H&M data for collaborative and content-based filtering techniques for recommenders.

3. Use the two-tower network to Create user and item embeddings in the same vector space.

4. Implement an H&M real-time personalized recommender using the 4-stage recommender design and a vector database.

5. Use MLOps best practices, such as a feature store and a model registry.

6. Deploy the online inference pipeline to Kubernetes using KServe.

7. Deploy the offline ML pipelines to GitHub Actions.

8. Implement a web interface using Streamlit.

9. Improve the H&M real-time personalized recommender using LLMs.

With these skills, you'll become a ninja in building real-time personalized recommenders.