RecSys at Spotify // Sanket Gupta // #232

Join us at our first in-person conference on June 25, all about AI Quality: https://www.aiqualityconference.com/Sanket works as a Senior Machine Learning Engineer at Spotify, working on building end-to-end audio recommender systems. Models built by his team are used across Spotify in many different products, including Discover Weekly and Autoplay.MLOps podcast #232 with Sanket Gupta, Senior Machine Learning Engineer at Spotify // RecSys at Spotify.A big thank you to LatticeFlow for sponsoring this episode!// AbstractLLMs with foundational embeddings have changed the way we approach AI today. Instead of re-training models from scratch end-to-end, we instead rely on fine-tuning existing foundation models to perform transfer learning. Is there a similar approach we can take with recommender systems? In this episode, we can talk about: a) how Spotify builds and maintains large-scale recommender systems, b) how foundational user and item embeddings can enable transfer learning across multiple products, c) how we evaluate this system, and d) MLOps challenges with these systems// BioSanket works as a Senior Machine Learning Engineer on a team at Spotify building production-grade recommender systems. Models built by my team are being used in Autoplay, Daily Mix, Discover Weekly, etc.Currently, my passion is how to build systems to understand user taste - how do we balance long-term and short-term understanding of users to enable a great personalized experience? // MLOps Jobs board jobs.mlops.community// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksWebsite: https://sanketgupta.substack.com/Our paper on this topic, "Generalized User Representations for Transfer Learning": https://arxiv.org/abs/2403.00584 Sanket's blogs on Medium in the past: https://medium.com/@sanket107 --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Sanket on LinkedIn: www.linkedin.com/in/sanketgupta107Timestamps:[00:00] Sanket's preferred coffee[00:37] Takeaways[02:30] RecSys are RAGs[06:22] Evaluating RecSys parallel to RAGs[07:13] Music RecSys Optimization[09:46] Dealing with cold start problems[12:18] Quantity of models in the recommender systems[13:09] Radio models[16:24] Evaluation system[20:25] Infrastructure support[21:25] Transfer learning[23:53] Vector database features[25:31] Listening History Balance[26:35 - 28:06] LatticeFlow Ad[28:07] The beauty of embeddings[30:13] Shift to real-time recommendation[34:05] Vector Database Architecture Options[35:30] Embeddings drive personalized[40:16] Feature Stores vs Vector Databases[42:33] Spotify product integration strategy[45:38] Staying up to date with new features[47:53] Speed vs Relevance metrics[49:40] Wrap up