This question is basically six questions duct-taped together.
Start by clarifying requirements and constraints, then propose a modular architecture with separate components for taxonomy management, classification, drift detection, human review, experimentation, and rollout. Emphasize trade-offs, iterative development, and how you'd measure success and ensure safety.
Pro tip: Anchor your design around a feedback loop: use human review not just for corrections but as a source of labeled data to continuously improve the classifier, and treat taxonomy changes as versioned schema migrations to avoid breaking downstream consumers.
Ask about scale (items/day, categories), latency needs, accuracy targets, and existing systems. Understand how categories are used (search, recommendations) to prioritize.
Propose a versioned taxonomy service with APIs for CRUD operations, hierarchy support, and change history. Discuss how to handle taxonomy evolution (merges, splits) without disrupting classification.
Outline a hybrid approach: rule-based for known patterns, ML (e.g., fine-tuned transformer) for scalability, and active learning to prioritize uncertain cases for human review.
Monitor data drift (input distribution) and concept drift (accuracy over time) using statistical tests and performance metrics. Route low-confidence or anomalous items to human reviewers, and feed corrections back into training.
Use A/B testing to compare new models/taxonomies against baseline, with guardrail metrics (e.g., user engagement, error rates). Roll out gradually via canary releases and feature flags, with rollback plans.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.