Differences between monitoring a traditional REST API and a production ML model
WHAT IT TESTS: Awareness that ML fails via data decay, not code bugs. ANSWER OUTLINE: Contrast latency/errors with ML signals like data drift and training-serving skew against baselines, noting ground truth delays.
WHAT IT TESTS: Whether you understand ML degrades from data distribution changes, not just code defects. ANSWER OUTLINE: First, contrast deterministic latency and error monitoring with probabilistic ML signals like data drift, prediction drift, and training-serving skew against baseline distributions. Second, note that model performance relies on delayed ground truth labels, unlike immediate API errors. RED FLAG: Treating the model as a standard microservice and citing only operational metrics while ignoring input distribution shifts.
Read the original → learn.microsoft.com
- #mlops
- #monitoring
- #machine-learning
- #interview
- #infrastructure
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