An MLOps team at a financial institution is responsible for a deployed credit default prediction model. The ground truth for a loan's outcome (default or paid) is only confirmed after 12-24 months, creating a significant feedback delay. The team needs a proactive strategy to detect potential model performance degradation long before the ground truth labels are available. Which of the following approaches is the MOST effective for early detection of model staleness?
Implement a fixed, semi-annual retraining schedule using the most recent data for which ground truth is available.
Perform daily backtesting by running the model on the previous day's data and comparing it to newly arrived ground truth.
Focus exclusively on operational metrics such as prediction latency, throughput, and system error rates.
Analyze the statistical distributions of input features and output predictions to monitor for data and concept drift.
The correct answer is to analyze the statistical distributions of both the input features and the model's output predictions against a baseline from the training or a previous validation dataset. In scenarios with a significant delay in obtaining ground truth, direct performance metrics like accuracy or F1-score cannot be calculated in real-time. Therefore, monitoring for data drift (changes in input feature distributions) and concept drift (which can be inferred from changes in the prediction distribution) serves as a critical proxy. A significant shift in these distributions, often measured using techniques like Population Stability Index (PSI) or the Kolmogorov-Smirnov test, indicates that the production data is different from the training data, suggesting the model's performance may be degrading. Monitoring only operational metrics like latency does not provide insight into the model's predictive accuracy. Backtesting with historical data is a validation technique, not continuous monitoring, and it would still rely on aged ground truth. Relying solely on periodic, scheduled retraining without a trigger based on monitoring can be inefficient, as it might happen too late or when it's not needed.
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What is data drift and how is it detected?
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What is concept drift and why is it important in monitoring models?
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What are the advantages of statistical distribution monitoring over operational metrics?