Google Professional Machine Learning Engineer
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You are a Machine Learning Engineer at a retail company that uses a DNN regression model to predict customer spending. The model was deployed six months ago and initially showed high accuracy. However, recent evaluations indicate a significant drop in performance due to shifts in customer behavior and input data distribution caused by seasonal changes and a new marketing campaign. The company requires a solution that not only addresses the current performance drop but also prevents future discrepancies with minimal manual intervention and cost. Which of the following strategies is the MOST effective to achieve this goal? (Choose one)
Explanation:
Implementing a continuous monitoring system for data drift and concept drift is the most effective strategy because it proactively addresses the root cause of the performance drop—changes in input data distribution and customer behavior. Automated alerts and retraining processes ensure the model adapts to these changes in real-time, maintaining high accuracy without requiring constant manual intervention. This approach is both cost-effective and scalable, aligning with the company's requirements for minimal manual intervention and cost. Feature selection or engineering alone does not address the dynamic nature of data drift, and periodic retraining without monitoring may not catch shifts in data distribution promptly.