Recently launched my first end-to-end ML app! A film recommender system based on matrix factorization, built for Letterboxd users.

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  • matrix-factorization

    Library for matrix factorization for recommender systems using collaborative filtering

    I originally tried using RiverML, which is dedicated to online ML, but after a ton of tweaking I still wasn't satisfied. In the end, I used the matrix-factorization library, which is not at all flashy but worked much, much better. By adjusting the learning rate and epochs for feeding new ratings into the model I can adjust how "personalized" the ratings are, and after a few days of messing with it I got it where I wanted it.

  • fastapi

    FastAPI framework, high performance, easy to learn, fast to code, ready for production

    This project is built with Streamlit for the front-end, FastAPI for the back-end and both are in Docker containers being run on AWS ECS.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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