Design an item category prediction system
Company: Walmart Labs
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: easy
Interview Round: Onsite
Design an end-to-end ML system that predicts an **item’s category/type** (multi-class or hierarchical classification), e.g., assigning an e-commerce listing to a taxonomy like `Electronics > Headphones`.
### What to cover
- Inputs available at prediction time (title, description, images, attributes, seller signals, etc.)
- Training data and labeling strategy (including noisy labels)
- Model choices (text-only vs multimodal; flat vs hierarchical)
- Offline evaluation metrics and online success metrics
- Serving architecture and latency/throughput targets
- Handling cold-start items, new categories, and class imbalance
- Monitoring, drift detection, and retraining/feedback loops
- Human-in-the-loop / moderation workflow for low-confidence cases
Quick Answer: This question evaluates a candidate's ability to design end-to-end machine learning systems for multi-class or hierarchical item categorization, encompassing competencies in feature engineering, multimodal modeling, labeling and data quality strategies, evaluation metrics, serving architecture, scalability, and operational monitoring.