How Pandora deploys machine learning models into production with Amelia and Filip
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/
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 🌟
📍 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:
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:
In this episode, Emily and Lukas dive into the problems with bigger and bigger language models, the difference between form and meaning, the limits of benchmarks, and why it's important to name the languages we study.
Show notes (links to papers and transcript):...
Jeff talks about building Facebook's early data team, founding Cloudera, and transitioning into biomedicine with Hammer Lab and Related Sciences.
Josh explains how astronomy and machine learning have informed each other, their current limitations, and where their intersection goes from here.
Josh is a Professor of Astronomy and Chair of the Astronomy Department at UC Berkeley. His research interests include the intersection of...