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.

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MLOps podcast #232 with Sanket Gupta, Senior Machine Learning Engineer at Spotify // Foundational Embeddings for Transfer Learning in Recommender Systems.

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.

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// 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