D. Sculley — Technical Debt, Trade-offs, and Kaggle
Show notes
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 Intro 1:02 Machine learning and technical debt 11:18 MLOps, increased stakes, and realistic expectations 19:12 Evaluating models methodically 25:32 Kaggle's role in the ML world 33:34 Kaggle competitions, datasets, and notebooks 38:49 Why Kaggle is like a rain forest 44:25 Possible future directions for Kaggle 46:50 Healthy competitions and self-growth 48:44 Kaggle's relevance in a compute-heavy future 53:49 AutoML vs. human judgment 56:06 After a model goes into production 1:00:00 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
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