Model Performance Analysis and Root Cause Analysis Questions
Techniques for diagnosing and troubleshooting production ML models, including monitoring metrics such as accuracy, precision, recall, ROC-AUC, latency and throughput; detecting data drift, feature drift, data quality issues, and model drift. Covers root-cause analysis across data, features, model behavior, and infrastructure, instrumentation and profiling, error analysis, ablation studies, and reproducibility. Includes remediation strategies to improve model reliability, performance, and governance in production systems.
MediumTechnical
0 practiced
Design instrumentation to profile end-to-end inference latency and per-component latency (preprocessing, model inference, postprocessing, network). Describe tracing/span design, tools to collect traces at scale, storage and aggregation strategy for p50/p95/p99, and how to use traces to pinpoint the root cause of latency spikes.
EasySystem Design
0 practiced
List and justify a minimum set of metrics and logs you would instrument for an online ML model to maintain observability. Include model-level metrics (e.g., prediction distributions), data-quality signals (e.g., null rates), and infrastructure metrics (e.g., p95 latency). For each item, describe how it helps detect common production issues.
MediumTechnical
0 practiced
Implement a rolling-window Kolmogorov–Smirnov (KS) test in Python to detect distribution drift for a streaming numeric feature. The function should accept a reference sample, an iterator/stream of new values, a window size, and an alpha threshold; it should yield timestamps (or indexes) where the KS p-value drops below alpha. Describe performance considerations for streaming data.
HardTechnical
0 practiced
Model predictions differ between training/offline evaluation and production inference even for identical raw inputs. List potential causes (e.g., preprocessing differences, library or operator version mismatch, mixed precision, dropout/batchnorm behavior, random seeds) and describe a step-by-step debugging strategy to reproduce and fix the mismatch in a controlled environment.
HardSystem Design
0 practiced
Design an automated root-cause analysis (RCA) pipeline that consumes model predictions, per-feature histograms, system traces, and business KPIs, and outputs a ranked list of probable causes with confidence scores. Describe the data schema, features for the RCA model/heuristics, candidate-generation strategies, ranking approach (heuristic vs supervised), and the human-in-the-loop review flow.
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