Which approach will meet these requirements with the LEAST operational overhead?
Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster. Apply k-fold cross-validation methods to the algorithm.
Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross-validation methods to the algorithm.
Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.
Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.
Explanations:
Using a bootstrap script to install scikit-learn on an Amazon EMR cluster adds significant operational overhead in terms of setup and management of the cluster, especially for a single binary classification task. K-fold cross-validation will also require additional coding and resource management, making it less efficient compared to other options.
While deploying Amazon SageMaker with prebuilt Docker images is a straightforward method, it still requires manual management of cross-validation and hyperparameter tuning, which may involve more operational overhead than necessary for optimizing AUC.
Amazon SageMaker automatic model tuning (AMT) allows for efficient hyperparameter optimization with minimal operational overhead. It automatically handles the training and validation process, enabling the ML specialist to focus on specifying hyperparameter ranges, leading to better model performance with less manual intervention.
Subscribing to an AUC algorithm from AWS Marketplace could provide a solution, but it may not allow the same level of customization for hyperparameter tuning as AMT. Moreover, relying on third-party algorithms could introduce compatibility and integration issues, increasing operational overhead.
As I recall, the answer is:
Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.