{"podcast":{"title":"Gradient Dissent: Conversations on AI","slug":"gradient-dissent","podcast_index_feed_id":1020509,"rss_url":"https://feeds.captivate.fm/gradient-dissent/","website_url":"https://wandb.ai/site/resources/podcast","image_url":"https://artwork.captivate.fm/25fd1181-b46e-459b-85a5-d397eec4cdcf/JDLDW81K-wlJoAWL7ZnxLdTp.jpg","author":"Lukas Biewald","episode_count":136,"summary":"Join Lukas Biewald on Gradient Dissent, an AI-focused podcast brought to you by Weights & Biases. Dive into fascinating conversations with industry giants from NVIDIA, Meta, Google, Lyft, OpenAI, and more. Explore the cutting-edge of AI and learn the intricacies of bringing models into production.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/gradient-dissent"},"episode":{"title":"Amelia & Filip — How Pandora Deploys ML Models into Production","slug":"amelia-filip-how-pandora-deploys-ml-models-into-production","published_at":"2021-07-01T19:00:00+00:00","page_url":"https://stenobird.com/podcast/gradient-dissent/amelia-filip-how-pandora-deploys-ml-models-into-production","show_page_url":"https://stenobird.com/podcast/gradient-dissent","url":"https://wandb.ai/site/resources/podcast","audio_url":"https://podcasts.captivate.fm/media/9f63f168-96fb-419c-8457-90c791db212b/1068496105-wandb-gd-amelia-and-filip.mp3","summary":"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…","meta_description":"Amelia and Filip give insights into the recommender systems powering Pandora, from developing models to balancing effectiveness and efficiency in producti…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2449,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/gradient-dissent/episodes/amelia-filip-how-pandora-deploys-ml-models-into-production/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/gradient-dissent/amelia-filip-how-pandora-deploys-ml-models-into-production.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}