I started with data quality and worked outward, which in retrospect was probably the right call since everything downstream breaks if your inputs are garbage.
Start by clarifying the ML use case (e.g., player performance prediction, lineup optimization) and the system's scale. Then structure your answer around a layered monitoring architecture: data quality, drift detection, model performance, and outlier detection, with feedback loops for root cause analysis and automated responses.
Pro tip: Emphasize the importance of establishing baseline metrics and thresholds during model deployment, and discuss how to prioritize alerts to avoid alert fatigue in a high-scale environment like Meta.
Ask questions to understand the ML model's purpose, data sources, update frequency, and business impact. Define what 'degradation' means in this context (e.g., accuracy drop, increased latency).
Outline checks for missing values, schema changes, range violations, and duplicate records. Include data freshness and completeness metrics, and how to handle anomalies.
Describe methods for detecting data drift (e.g., PSI, KL divergence) and concept drift (e.g., ADWIN, page-Hinkley). Specify which features to monitor and how to set thresholds.
Define performance metrics (e.g., RMSE, accuracy) and how to track them over time. Explain outlier detection techniques (e.g., isolation forests, z-scores) for predictions and inputs.
Design an alerting system with severity levels and automated root cause analysis (e.g., correlation of drift with performance drops). Include dashboards and feedback loops for retraining.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.