Learn how to effectively troubleshoot errors encountered when launching custom images in Amazon SageMaker Studio using Amazon CloudWatch logs. Discover the key service for accessing error logs and resolving issues in your machine learning workflows.
Table of Contents
Question
A healthcare company wants to create a machine learning (ML) model to predict patient outcomes. A data science team developed an ML model by using a custom ML library. The company wants to use Amazon SageMaker to train this model. The data science team creates a custom SageMaker image to train the model. When the team tries to launch the custom image in SageMaker Studio, the data scientists encounter an error within the application.
Which service can the data scientists use to access the logs for this error?
A. Amazon S3
B. Amazon Elastic Block Store (Amazon EBS)
C. AWS CloudTrail
D. Amazon CloudWatch
Answer
D. Amazon CloudWatch
Explanation
When encountering an error while launching a custom image in Amazon SageMaker Studio, the data scientists should use Amazon CloudWatch to access the logs and troubleshoot the issue. CloudWatch is a monitoring and observability service that collects log data from various AWS services, including Amazon SageMaker.
SageMaker automatically sends log data to CloudWatch Logs, allowing users to monitor and debug issues related to their machine learning workflows. By accessing the relevant log group and log stream in CloudWatch, the data scientists can view detailed logs of the error that occurred during the launch of the custom image.
CloudWatch provides a centralized location for storing and analyzing log data, making it the go-to service for troubleshooting errors in SageMaker Studio. The other options mentioned are not suitable for this specific scenario:
- Amazon S3 is an object storage service and does not store application logs.
- Amazon Elastic Block Store (EBS) provides block-level storage volumes for EC2 instances and is not used for logging purposes.
- AWS CloudTrail tracks API calls and account activity, but it does not capture application-level logs.
By utilizing CloudWatch Logs, the data scientists can identify the root cause of the error, such as missing dependencies or configuration issues in the custom image, and take appropriate steps to resolve the problem.
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