ML Engineer Pipeline Questions I Prep For
Five pipeline questions I bring with me to ML engineer loops. Training-serving skew, label leakage, batch vs streaming features, retraining cadence, and a small idempotent upsert into the feature store.
By @maxreyes
February 1, 2026
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Updated August 12, 2026
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The interviewer asked me to walk through how I detect training-serving skew in a model that scores credit applications. They wanted code that compares two summaries: one from the offline training set and one from the live request stream.
From my prep doc
I hold one numeric feature (income_log) and check the population stability index between offline and online buckets. PSI above 0.25 is my pager-worthy threshold.
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