How to Build a Full-Stack Recommender System

Outerbounds

Jacopo Tagliabue, RecSys expert and former Director of AI at Coveo, will join Hugo Bowne-Anderson, Outerbounds’ Head of DevRel, in our first live code along session to dive into how to build a production-grade RecSys, the goal being to develop a relatively simple, effective, and general pipeline for sequential recommendations. We’ll show how you can use popular open-source libraries and tools including DuckDB, Gensim, Metaflow, and Keras to build a fully working cloud endpoint that serves predictions in real time, starting from raw data. We’ll be using the <a href="https://outerbounds.com/docs/sandbox/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Metaflow sandboxes</a> so you can easily code along (think Colab but for MLOps)!Using the <a href="https://www.kaggle.com/datasets/andrewmvd/spotify-playlists?resource=download" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Spotify Playlists dataset</a>, you’ll learn how to<ul><li>take a recommender system idea from prototype to real-time production;</li><li>leverage Metaflow to train different versions of the same model and pick the best one;</li><li>use Metaflow cards to save important details about model performance;</li><li>package a representation of your data in a Keras object that you can deploy directly from the flow to a cloud endpoint with AWS Sagemaker.</li></ul>We hope to see you there!

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How to Build a Full-Stack Recommender System

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How to Build a Full-Stack Recommender System

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