I started rattling off accuracy and loss and the interviewer just kind of waited.
Start by framing metrics into three layers: system health, model performance, and business impact. Emphasize that at Netflix, you need to balance technical metrics with product outcomes and consider the unique constraints of a streaming service. Conclude by discussing how you use these metrics to drive iteration and improvements.
Pro tip: Show that you understand the trade-offs between latency and accuracy in a real-time recommendation system, and mention how you'd set up automated alerts for metric degradation to enable proactive fixes.
Monitor infrastructure and operational metrics such as latency, throughput, error rates, and resource utilization to ensure the model serves reliably at scale.
Track prediction quality metrics like accuracy, precision, recall, AUC, and calibration, as well as data drift and concept drift to detect degradation.
Measure how the model affects key product metrics such as user engagement, retention, streaming hours, and content discovery.
Use A/B tests to compare model variants and measure incremental impact on business metrics, ensuring statistical significance.
Establish a process to regularly review metrics, identify issues, and retrain or update the model based on insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.