A deployed machine learning model exhibits unpredictable performance degradation over time, sometimes degrading quickly and sometimes slowly. The goal is to find a cost-effective method to maintain high performance by retraining the model at optimal intervals.
The suggested answer is D, which involves regularly checking for discrepancies between the training data and live data (training-serving skew). This proactive approach allows for early detection of performance issues and targeted retraining, optimizing cost and performance.
You deployed an ML model into production a year ago. Every month, you collect all raw requests that were sent to your model prediction service during the previous month. You send a subset of these requests to a human labeling service to evaluate your model’s performance. After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance. The labeling service is costly, but you also need to avoid large performance degradations. You want to determine how often you should retrain your model to maintain a high level of performance while minimizing cost. What should you do?
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