AI System Design Interview
Practice AI and ML system design across data pipelines, retrieval, inference, evaluation, monitoring, latency, reliability, and cost trade-offs.
Connect the offline and online paths
- Define product behaviorState what the model produces and how quality is measured.
- Separate offline and onlineShow ingestion, preparation, deployment, inference, and feedback.
- Budget latency, quality, and costMake the serving trade-offs explicit.
- Design evaluation and observabilityTrack data quality, drift, failures, and user impact.
- Plan degradation and rollbackExplain how the product behaves when an AI dependency fails.