AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 141
A medical company ingests streams of data from devices that monitor patients’ vital signs. The company uses Amazon SageMaker and plans to prepare ML models to predict adverse events for patients. The dataset is large with thousands of features.
An ML engineer needs to run several hundred training iterations with different sets of features, different algorithms, and many potential parameters. The ML engineer must implement a solution to log the characteristics and results of each training iteration.
Which solution will meet these requirements with the LEAST implementation effort?
Answer options
- A. Use Amazon CloudWatch to create custom metrics for the characteristics of each iteration.
- B. Write the characteristics of each iteration to logs in Amazon S3. Use AWS Glue and Amazon Athena to search the logs.
- C. Use the SageMaker Model Registry to track the characteristics and results of each iteration.
- D. Use SageMaker Experiments to track the characteristics and results of each iteration.
Correct answer: D
Explanation
The correct answer is D because SageMaker Experiments is specifically designed to track and log the characteristics and results of machine learning training iterations with minimal implementation effort. Option A is incorrect as it requires manual metric creation, while option B involves additional steps with S3, Glue, and Athena, making it more complex. Option C, while useful for model management, does not provide the same level of detailed iteration tracking as SageMaker Experiments.