Design an ML Model for Interview Recommendation Pipeline

Q: Design an ML Model for Interview Recommendation Pipeline

This question evaluates competency in Machine Learning system design, specifically recommender systems, feature engineering for high-cardinality and sparse interactions, algorithm selection, and MLOps concerns such as low-latency serving, deployment, retraining, and monitoring.

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Question

Scenario

You are designing and deploying an ML model that mirrors a real-world recommendation pipeline serving a large product catalog with strict latency constraints and high traffic.

Task

Answer the following, as if describing your own most recent production system. If needed, make reasonable assumptions and state them.

1) Feature Engineering

What entities and features did you create (user, item, context, sequence, interaction)?
How did you encode high-cardinality categorical variables and sparse interactions?
How did you prevent data leakage and handle missing/rare values?

2) Algorithm Choice and Alternatives

Which algorithm(s) did you choose and why?
What alternatives did you evaluate and why were they rejected (e.g., latency, complexity, accuracy, ops cost)?

3) End-to-End Workflow

Describe the pipeline from raw data ingestion to online inference and monitoring:

Data sources and labeling
Offline training, validation, and metrics
Packaging, deployment, and real-time serving
Retraining cadence and triggers
Monitoring (data, model, system) and alerting

Hints

Discuss trade-offs (e.g., latency vs. accuracy, complexity vs. maintainability)
Explain retraining cadence and rollout strategy (canary/shadow/A-B testing)
Detail your online monitoring strategy and guardrails