Which is the MOST suitable predictive model that can be deployed into production?

A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training.

Which is the MOST suitable predictive model that can be deployed into production?
A . 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.
B . This data requires an unsupervised learning algorithm Use Amazon SageMaker k-means to cluster the data
C . 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.
D . This data is already formulated as a time series Use Amazon SageMaker seq2seq to model the time series.

View Answer

Answer: A

Explanation:

A recurrent neural network (RNN) is a type of neural network that can process sequential data, such as time series, by maintaining a hidden state that captures the temporal dependencies between the inputs. RNNs are well suited for predicting future events based on past observations, such as forecasting engine failures based on sensor readings. To train an RNN model, the data needs to be labeled with the target variable, which in this case is the type and time of the engine fault. This makes the problem a supervised learning problem, where the goal is to learn a mapping from the input sequence (sensor readings) to the output sequence (engine faults). By using an RNN model, the manufacturer can leverage the temporal information in the data and detect patterns that indicate when an engine might need maintenance for a certain fault.

Reference: Recurrent Neural Networks – Amazon SageMaker

Use Amazon SageMaker Built-in Algorithms or Pre-trained Models Recurrent Neural Network Definition | DeepAI

What are Recurrent Neural Networks? An Ultimate Guide for Newbies! Lee and Carter go Machine Learning: Recurrent Neural Networks – SSRN