Episode Details

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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!

// Abstract

LLMs 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

// Bio

Sanket 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?

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jobs.mlops.community

// MLOps Swag/Merch

https://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

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Timestamps:

[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 recommen