Exam AWS Certified Machine Learning - Specialty topic 1 question 307 discussion

from copilot - Amazon Lookout for Equipment is specifically designed for predictive maintenance and can automatically detect anomalies in sensor data, making it a suitable choice for this scenario. It minimizes the need for manual intervention and leverages advanced machine learning models to identify and handle outliers efficiently.

https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-analyses.html#data-wrangler-time-series-anomaly-detection

Data Wrangler can do it all

Anomaly detection visualization feature in SageMaker Data Wrangler is designed to identify outliers in the dataset based on sensor data parameters such as temperature and pressure. By visually inspecting the anomalies, the ML specialist can easily identify and remove outliers using transformations within Data Wrangler data flows, minimizing operational overhead.

Anomaly detection visualization feature in SageMaker Data Wrangler is designed to identify outliers in the dataset based on sensor data parameters such as temperature and pressure. By visually inspecting the anomalies, the ML specialist can easily identify and remove outliers using transformations within Data Wrangler data flows, minimizing operational overhead.

Going with C

agree with C

Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options. References: Amazon SageMaker Data Wrangler - Amazon Web Services (AWS) Anomaly Detection Visualization - Amazon SageMaker Transform Data - Amazon SageMaker