Debug a Machine Learning Pipeline

Q: Debug a Machine Learning Pipeline

This question evaluates a candidate's ability to diagnose production machine learning failures, covering competencies in data quality, data and concept drift detection, model versioning and deployment checks, and operational debugging within an MLOps context.

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Question

Debugging a Sudden Accuracy Drop in a Deployed ML Pipeline

Context

You are on-call for a production machine learning service. Monitoring alerts show that model accuracy, which had been stable, suddenly dropped after a deployment. Labels may arrive with a delay, and traffic patterns can shift over time. You need to systematically diagnose and fix the issue.

Task

Describe a step-by-step process to debug this accuracy drop, including:

How you would triage and prioritize (e.g., rollback, canary, guardrails).
The tools and logs you would inspect.
The metrics and statistical tests you would compute (for both data and model performance).
How you would isolate root cause across data, model, code/config, infra, and labels.
How you would validate the fix and prevent regressions.

Be specific about:

Data quality, drift, and schema checks.
Training vs. inference preprocessing parity.
Model registry/versioning and environment differences.
Label delays and evaluation correctness.
Offline reproduction and A/B/shadow testing strategies.

Debug a Machine Learning Pipeline

Debugging a Sudden Accuracy Drop in a Deployed ML Pipeline

Context

Task

Solution

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Debug a Machine Learning Pipeline

Overview

Debugging a Sudden Accuracy Drop in a Deployed ML Pipeline

Context

Task

Solution

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