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In this ML mock interview, a FanDuel machine learning engineer designs a machine learning system for personalizing music recommendations on Spotify, highlighting data preparations, metrics, model choices, production considerations, and room for improvement.

Chapters (Powered by ChapterMe) -
00:00 - Intro
01:52 - Data engagement, clicks, users, metadata
06:32 - Building models in batches or real-time
07:05 - Data pipeline design and features overview
09:19 - Data normalization for Spotify users clicks
12:13 - Data cleanup and age group predictions
16:11 - Content filtering and collaborative filtering for recommendation
20:07 - Choosing model, collaborative filtering, pitfalls
21:34 - Importance of training, validation, and production
22:15 - Cloud computing simplifies model testing
23:50 - Metrics and model success
24:44 - Engagement and churn metrics determine models performance
26:10 - Key insights for recommending artists
30:04 - ML interview analysis key takeaways
31:18 - Game plan, production, and detail improvement

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