Which is the MOST suitable predictive model that can be deployed into production?
Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
This data requires an unsupervised learning algorithm. Use Amazon SageMaker k-means to cluster the data.
Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a convolutional neural network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
This data is already formulated as a time series. Use Amazon SageMaker seq2seq to model the time series.
Explanations:
Adding labels to indicate engine faults turns the problem into a supervised learning task. An RNN is well-suited for time series data, especially when predicting future events like engine failures based on temporal patterns.
Unsupervised learning algorithms like k-means are used for clustering similar data points, not for predicting specific future events. This approach would not be ideal for forecasting engine failures.
CNNs are typically used for image recognition or spatial data, not time series forecasting. RNNs are more appropriate for sequential data like engine sensor readings over time.
While seq2seq models are designed for time series prediction, they are typically used for tasks like sequence translation or generation, which may not be the best choice for predicting engine faults from sensor data. RNNs would be a better choice.
As I see it, the answer is:
Add labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a recurrent neural network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.