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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! LatticeFlow - https://latticeflow.ai/

// 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
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
https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Website: 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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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