D. Sculley — Technical Debt, Trade-offs, and Kaggle

D. Sculley is CEO of Kaggle, the beloved and well-known data science and machine learning community.D. discusses his influential 2015 paper "Machine Learning: The High Interest Credit Card of Technical Debt" and what the current challenges of deploying models in the real world are now, in 2022. Then, D. and Lukas chat about why Kaggle is like a rain forest, and about Kaggle's historic, current, and potential future roles in the broader machine learning community.Show notes (transcript and links): http://wandb.me/gd-d-sculley---⏳ Timestamps:0:00 Intro1:02 Machine learning and technical debt11:18 MLOps, increased stakes, and realistic expectations19:12 Evaluating models methodically25:32 Kaggle's role in the ML world33:34 Kaggle competitions, datasets, and notebooks38:49 Why Kaggle is like a rain forest44:25 Possible future directions for Kaggle46:50 Healthy competitions and self-growth48:44 Kaggle's relevance in a compute-heavy future53:49 AutoML vs. human judgment56:06 After a model goes into production1:00:00 Outro---Connect with D. and Kaggle:📍 D. on LinkedIn: https://www.linkedin.com/in/d-sculley-90467310/📍 Kaggle on Twitter: https://twitter.com/kaggle---Links:📍 "Machine Learning: The High Interest Credit Card of Technical Debt" (Sculley et al. 2014): https://research.google/pubs/pub43146/---💬 Host: Lukas Biewald📹 Producers: Riley Fields, Angelica Pan, Anish Shah, Lavanya Shukla---Subscribe and listen to our podcast today!👉 Apple Podcasts: http://wandb.me/apple-podcasts​​👉 Google Podcasts: http://wandb.me/google-podcasts​👉 Spotify: http://wandb.me/spotify​