Amelia and Filip give insights into the recommender systems powering Pandora, from developing models to balancing effectiveness and efficiency in production.

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Amelia Nybakke is a Software Engineer at Pandora. Her team is responsible for the production system that serves models to listeners.

Filip Korzeniowski is a Senior Scientist at Pandora working on recommender systems. Before that, he was a PhD student working on deep neural networks for acoustic and language modeling applied to musical audio recordings.

Connect with Amelia and Filip:

📍 Amelia's LinkedIn: https://www.linkedin.com/in/amelia-nybakke-60bba5107/

📍 Filip's LinkedIn: https://www.linkedin.com/in/filip-korzeniowski-28b33815a/

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⏳ Timestamps:

0:00 Sneak peek, intro

0:42 What type of ML models are at Pandora?

3:39 What makes two songs similar or not similar?

7:33 Improving models and A/B testing

8:52 Chaining, retraining, versioning, and tracking models

13:29 Useful development tools

15:10 Debugging models

18:28 Communicating progress

20:33 Tuning and improving models

23:08 How Pandora puts models into production

29:45 Bias in ML models

36:01 Repetition vs novelty in recommended songs

38:01 The bottlenecks of deployment

🌟 Transcript: http://wandb.me/gd-amelia-and-filip 🌟

Links:

📍 Amelia's "Women's History Month" playlist: https://www.pandora.com/playlist/PL:1407374934299927:100514833

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