How Pandora deploys machine learning models into production with Amelia and Filip
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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: 📍 Filip's LinkedIn: --- ⏳ 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: 🌟 Links: 📍 Amelia's "Women's History Month" playlist: --- Get our podcast on these platforms: 👉 Apple Podcasts:​​ 👉 Spotify:​ 👉 Google Podcasts:​​ 👉 YouTube:​​ 👉 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:
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