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!

// 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?

// MLOps Jobs board

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

--------------- ✌️Connect With Us ✌️ -------------

Join our Slack community: https://go.mlops.community/slack

Follow us on Twitter: @mlopscommunity

Sign up for the next meetup: https://go.mlops.community/register

Catch 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/sanketgupta107

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 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