MLS-C01 Question 181
Single answerA data scientist is training a machine learning model using Amazon SageMaker. The model's performance is sensitive to various hyperparameters, such as learning rate, number of layers, and batch size. The scientist wants to automate the hyperparameter tuning process to find the optimal set of hyperparameters for the best model performance. Which feature or method in SageMaker should the data scientist use?
- A
SageMaker Automatic Model Tuning
- B
SageMaker Neo
- C
SageMaker Debugger
- D
SageMaker Data Wrangler
Show answer and explanation
Correct answer: A
Explanation
Hyperparameter optimization is a key step in improving the performance of machine learning models. Amazon SageMaker provides Automatic Model Tuning, a feature designed specifically for this purpose. By leveraging this capability, the data scientist can efficiently explore the hyperparameter space and identify the configuration that yields the best model performance.
- A. Correct.
SageMaker Automatic Model Tuning is the correct feature for automating hyperparameter optimization. It uses techniques such as Bayesian optimization to efficiently search for the best set of hyperparameters.
- B. Incorrect.
SageMaker Neo is used for optimizing model inference performance, not for hyperparameter tuning.
- C. Incorrect.
SageMaker Debugger helps in monitoring and debugging training jobs but does not perform hyperparameter optimization.
- D. Incorrect.
SageMaker Data Wrangler is used for preparing and transforming data, not for hyperparameter tuning.