Amelia and Filip give insights into the recommender systems powering Pandora, from developing models to balancing effectiveness and efficiency in production.
---
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/
---
⏳ 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
---
Get our podcast on these platforms:
👉 Apple Podcasts: http://wandb.me/apple-podcasts
👉 Spotify: http://wandb.me/spotify
👉 Google Podcasts: http://wandb.me/google-podcasts
👉 YouTube: http://wandb.me/youtube
👉 Soundcloud: http://wandb.me/soundcloud
Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
http://wandb.me/slack
Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
https://wandb.ai/fully-connected