Machine Learning Advanced Pro

Case Study: Ad Click-Through Rate Prediction (Meta / Google Ads-style)

Walk through a full ML system design interview answer for auction-grade pCTR prediction

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Model interview answer for ad click-through rate prediction: why calibrated pCTR drives the auction, delayed labels and negative downsampling, sparse ID features, LR-to-DLRM model evolution, calibration monitoring, online learning and a low-latency serving architecture.

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Practice questions (5)

Why Calibration Matters More Than AUC in an Ad Auction

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Negative Downsampling and Re-calibration

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Delayed Labels in a Streaming Trainer

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Fitting a Deep pCTR Model into 10 ms

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Cold-Start Ads and the Feedback Loop

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Quizzes (1)

Case Study: Ad Click-Through Rate Prediction Quiz

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