ML Data Pipelines & Feature Stores

Turn raw events into leak-free training sets and consistent serving features — the part of the ML interview that separates builders from talkers

Design the data side of an ML system: logging for train/serve parity, labelling strategies, point-in-time joins, negative sampling, batch vs streaming features, feature stores, training–serving skew, data validation, lineage and a worked notification-click pipeline.

Practice questions (5)

• Diagnosing a Point-in-Time Leak

  Intermediate · Free
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• Choosing an Attribution Window for Delayed Conversions

  Intermediate
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• Negative Sampling and Probability Recalibration

  Intermediate
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• Investigating Training–Serving Skew

  Advanced
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• Data Validation Gates and a Feature Backfill

  Intermediate
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Quizzes (1)