jacopotagliabue/recs-at-resonable-scale
Recommendations at "Reasonable Scale": joining dataOps with recSys through dbt, Merlin and Metaflow
This project helps a single machine learning specialist efficiently train, test, and deploy advanced recommendation systems, like those used to suggest fashion items. It takes raw customer purchase history and outputs personalized product recommendations, ready to be served to online shoppers. This is designed for ML practitioners who need to build and manage robust recommendation pipelines without deep infrastructure expertise.
241 stars. No commits in the last 6 months.
Use this if you are an ML specialist looking to build and deploy a cutting-edge deep learning recommender system using Python and SQL, without needing a dedicated DevOps team.
Not ideal if you are looking for a simple, out-of-the-box recommendation API without any pipeline configuration or model training.
Stars
241
Forks
14
Language
Python
License
MIT
Category
Last pushed
Apr 07, 2023
Commits (30d)
0
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