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Answer: Apply a simple statistical method, such as calculating z-scores, to automatically label the data as 'normal' or 'anomalous' based on historical performance, then use this labeled data to train a supervised learning model.
The most appropriate initial step is to apply a simple statistical method, such as calculating z-scores, to automatically label the data. This approach is scalable and cost-effective, as it does not require manual labeling of data. The labeled data can then be used to train a supervised learning model, which is more accurate and efficient for predictive maintenance. This method balances the need for quick implementation with the ability to improve over time as more data becomes available.
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In a large-scale data center, your team is tasked with developing a predictive maintenance solution to preempt server failures using unlabeled monitoring data. The solution must be scalable, cost-effective, and capable of handling real-time data streams. Given these constraints, what is the most appropriate initial step to take? Choose one correct option.
A
Hire a team of data analysts to manually label historical performance data and use this dataset to train a supervised learning model.
B
Implement a complex deep learning model to directly analyze the unlabeled data and predict failures without any initial labeling.
C
Apply a simple statistical method, such as calculating z-scores, to automatically label the data as 'normal' or 'anomalous' based on historical performance, then use this labeled data to train a supervised learning model.
D
Use an unsupervised learning algorithm to cluster the data into 'normal' and 'anomalous' groups without any initial labeling, and then monitor these clusters for changes over time.