decodingai-magazine/personalized-recommender-course
An open-source course teaching how to architect, build, and deploy a real-time personalized recommender system for H&M fashion using neural networks and MLOps.
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This hands-on course by Decoding AI in collaboration with Hopsworks guides learners through building a production-grade recommender system. It covers feature engineering with Polars, training two-tower neural network models, deploying on Kubernetes via KServe, and applying MLOps best practices using the Hopsworks AI Lakehouse. The course also explores LLM techniques for personalized recommendations in the fashion e-commerce domain.
Frequently asked
- What is decodingai-magazine/personalized-recommender-course?
- An open-source course teaching how to architect, build, and deploy a real-time personalized recommender system for H&M fashion using neural networks and MLOps.
- Is personalized-recommender-course open source?
- Yes — decodingai-magazine/personalized-recommender-course is open source, released under the MIT license.
- What language is personalized-recommender-course written in?
- decodingai-magazine/personalized-recommender-course is primarily written in Jupyter Notebook.
- How popular is personalized-recommender-course?
- decodingai-magazine/personalized-recommender-course has 645 stars on GitHub.
- Where can I find personalized-recommender-course?
- decodingai-magazine/personalized-recommender-course is on GitHub at https://github.com/decodingai-magazine/personalized-recommender-course.