Which solution will meet these requirements with the LEAST operational overhead?
Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon EMR. Use Amazon SageMaker Studio Classic to develop the model. Use Amazon Augmented Al (Amazon A2I) to check the model for bias before finalizing the model.
Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon EMR. Use Amazon SageMaker Clarify to develop the model. Use Amazon Augmented AI (Amazon A2I) to check the model for bias before finalizing the model.
Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon SageMaker Studio. Use Amazon SageMaker JumpStart to develop the model. Use Amazon SageMaker Clarify to check the model for bias before finalizing the model.
Process and reduce bias by using an Amazon SageMaker Studio notebook. Use Amazon SageMaker JumpStart to develop the model. Use Amazon SageMaker Model Monitor to check the model for bias before finalizing the model.
Explanations:
While SMOTE in Amazon EMR is an option for oversampling, Amazon SageMaker Studio Classic is outdated and not the recommended environment for model development today. Additionally, using Amazon A2I to check for bias is not the most efficient solution compared to using SageMaker Clarify.
Amazon EMR is not the optimal environment for oversampling, and the combination of SMOTE and Amazon A2I for bias detection is not the most streamlined or low-overhead approach. SageMaker Clarify would be a better tool for bias detection.
Using SMOTE in SageMaker Studio and SageMaker JumpStart for quick model development is efficient and meets the requirement for developing the model quickly. SageMaker Clarify is an ideal tool to check for bias before finalizing the model. This solution provides the least operational overhead.
Although SageMaker Studio and JumpStart are used for model development, using SageMaker Model Monitor for bias detection is overkill and not as streamlined as SageMaker Clarify. Model Monitor is more focused on post-deployment monitoring, not pre-model-finalization bias checks.
I measure that the answer is:
Process and reduce bias by using the synthetic minority oversampling technique (SMOTE) in Amazon SageMaker Studio. Use Amazon SageMaker JumpStart to develop the model. Use Amazon SageMaker Clarify to check the model for bias before finalizing the model.