Structure your answer around a layered testing strategy that progresses from offline evaluation to online experimentation, emphasizing how each layer mitigates different risks. Highlight the importance of aligning offline metrics with online business metrics and the role of continuous monitoring post-deployment.
Pro tip: At Meta, scale and low latency are critical, so mention how you test for performance and fairness at scale, and how you use counterfactual logging and interleaving to speed up experimentation.
Start with offline testing using a held-out test set and cross-validation to assess model performance on metrics like accuracy, precision, recall, and AUC. Ensure data splits are representative and consider temporal splits for time-series data.
Evaluate model robustness to distribution shifts, adversarial examples, and edge cases. Also, assess fairness across different user segments to avoid bias and ensure compliance with ethical guidelines.
Deploy the model to a small percentage of traffic in a controlled A/B test, comparing it against the current production model. Define primary and guardrail metrics, and ensure sufficient statistical power to detect meaningful differences.
Test the model's inference latency and throughput under production-like load to ensure it meets performance requirements. Use load testing and profiling to identify bottlenecks.
After full deployment, continuously monitor model performance, data drift, and business metrics. Set up alerts for anomalies and establish a feedback loop for retraining and improvement.
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