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

Gradient Dissent: Exploring Machine Learning, AI, Deep Learning, Computer Vision

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

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⏳ 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

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Connect with D. and Kaggle:

📍 D. on LinkedIn: https://www.linkedin.com/in/d-sculley-90467310/

📍 Kaggle on Twitter: https://twitter.com/kaggle

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Links:

📍 "Machine Learning: The High Interest Credit Card of Technical Debt" (Sculley et al. 2014): https://research.google/pubs/pub43146/

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💬 Host: Lukas Biewald

📹 Producers: Riley Fields, Angelica Pan, Anish Shah, Lavanya Shukla

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