Given the nature of the data and the business objective, which Amazon SageMaker built-in algorithm is the MOST SUITABLE for this use case?

You are a machine learning engineer working for a telecommunications company that needs to develop a predictive maintenance model. The goal is to predict when network equipment is likely to fail based on historical sensor data. The data includes features such as temperature, pressure, usage, and error rates recorded over time. The company wants to avoid unplanned downtime and optimize maintenance schedules by predicting failures just in time.

Given the nature of the data and the business objective, which Amazon SageMaker built-in algorithm is the MOST SUITABLE for this use case?
A . DeepAR Algorithm to forecast future equipment failures based on historical data
B . Time Series K-Means Algorithm to cluster similar patterns in the sensor data and predict failures
C . Random Cut Forest (RCF) Algorithm to detect anomalies in sensor data that may indicate impending failures
D . Linear Learner Algorithm to classify equipment status as ‘healthy’ or ‘at risk’ based on sensor readings

View Answer

Answer: C

Explanation:

Correct option:

Random Cut Forest (RCF) Algorithm to detect anomalies in sensor data that may indicate impending failures

Amazon SageMaker Random Cut Forest (RCF) is an unsupervised algorithm for detecting anomalous data points within a data set. These are observations which diverge from otherwise well-structured or patterned data. Anomalies can manifest as unexpected spikes in time series data, breaks in periodicity, or unclassifiable data points. They are easy to describe in that, when viewed in a plot, they are often easily distinguishable from the "regular" data. Including these anomalies in a data set can drastically increase the complexity of a machine learning task since the "regular" data can often be described with a simple model.

Random Cut Forest (RCF) is specifically designed for detecting anomalies in data. This algorithm excels at identifying unexpected patterns in sensor data that could indicate the early stages of equipment failure. It’s particularly well-suited for scenarios where you need to react to unusual behaviors in near-real-time.

Mapping use cases to built-in algorithms:

via – https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

Incorrect options:

DeepAR Algorithm to forecast future equipment failures based on historical data – DeepAR is designed for forecasting future time series data, which could be useful for predicting future equipment behavior. However, it is not primarily used for anomaly detection, which is critical for identifying unusual patterns that precede failures.

Linear Learner Algorithm to classify equipment status as ‘healthy’ or ‘at risk’ based on sensor readings – Linear Learner could be used for classification tasks, but predicting maintenance needs often involves detecting subtle anomalies rather than simple classification. Additionally, a binary classification model might not capture the complex patterns associated with potential failures.

Time Series K-Means Algorithm to cluster similar patterns in the sensor data and predict failures – Time Series K-Means can cluster similar time series patterns, but clustering alone does not provide the precision needed for real-time anomaly detection, which is crucial for predictive maintenance.

References:

https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html