{"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":"Sean Taylor — Business Decision Problems","slug":"sean-taylor-business-decision-problems","published_at":"2021-05-13T19:00:00+00:00","page_url":"https://stenobird.com/podcast/gradient-dissent/sean-taylor-business-decision-problems","show_page_url":"https://stenobird.com/podcast/gradient-dissent","url":"https://wandb.ai/site/resources/podcast","audio_url":"https://podcasts.captivate.fm/media/716d606c-3b4d-4846-b940-cd10fb490a8b/1041905833-wandb-gd-sean-taylor.mp3","summary":"Sean joins us to chat about ML models and tools at Lyft Rideshare Labs, Python vs R, time series forecasting with Prophet, and election forecasting. --- Sean Taylor is a Data Scientist at (and former Head of) Lyft Rideshare Labs, and specializes in methods for solving causal inference and business decision problems. Previously, he was a Research Scientist on Facebook's Core Data Science team. His interests include experiments, causal inference, statistics, machine learning, and economics. Connect with Sean: Personal website: https://seanjtaylor.com/ Twitter: https://twitter.com/seanjtaylor LinkedIn: https://www.linkedin.com/in/seanjtaylor/ --- Topics Discussed: 0:00 Sneak peek, intro 0:50 Pricing algorithms at Lyft 07:46 Loss functions and ETAs at Lyft 12:59 Models and tools at Lyft 20:46 Python vs R 25:30 Forecasting time series data with Prophet 33:06 Election forecasting and prediction markets 40:55 Comparing and evaluating models 43:22 Bottlenecks in going from research to production Transcript: http://wandb.me/gd-sean-taylor Links Discussed: \"How Lyft predicts a rider’s destination for better in-app experience\"\": https://eng.lyft.com/how-lyft-predicts-your-destination-with-attention-791146b0a439 Prophet: https://facebook.github.io/prophet/ Andrew Gelman's blog post \"Facebook's Prophet uses Stan\": https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/ Twitter thread \"Election forecasting using prediction markets\": https://twitter.com/seanjtaylor/status/1270899371706466304 \"An Updated Dynamic Bayesian Forecasting Model for the 2020 Election\": https://hdsr.mitpress.mit.edu/pub/nw1dzd02/release/1 --- Get our podcast on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts​​ Spotify: http://wandb.me/spotify​ Google Podcasts: http://wan…","meta_description":"Sean joins us to chat about ML models and tools at Lyft Rideshare Labs, Python vs R, time series forecasting with Prophet, and election forecasting. --- S…","key_points":[],"chapters":[],"topics":[],"duration_seconds":2741,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/gradient-dissent/episodes/sean-taylor-business-decision-problems/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/gradient-dissent/sean-taylor-business-decision-problems.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}