Which solution will meet these requirements with the MOST operational efficiency?
Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Use SageMaker Data Wrangler templates for visualization. Export the feature processing workflow to a SageMaker pipeline for automation.
Use an Amazon SageMaker notebook instance to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.
Use AWS Glue Studio with custom code to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.
Use Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package each feature transformation step into a separate AWS Lambda function. Use AWS Step Functions for workflow automation.
Explanations:
This option utilizes Amazon SageMaker Data Wrangler, which offers preconfigured transformations and visualization templates. It allows the team to easily explore feature transformations and visualize results. The feature processing workflow can be exported to a SageMaker pipeline, enabling efficient automation of the workflow.
While using a SageMaker notebook instance allows experimentation with feature transformations, it lacks the efficiency of preconfigured transformations. Saving transformations to S3 and using QuickSight for visualization adds complexity. Additionally, packaging processing steps into a Lambda function may not offer the best automation compared to pipeline capabilities in SageMaker.
AWS Glue Studio allows for custom transformations, but it may require more manual coding and setup, reducing operational efficiency. Although QuickSight can visualize data, this option lacks a straightforward way to automate the entire workflow compared to using SageMaker pipelines.
While this option utilizes SageMaker Data Wrangler for transformations and QuickSight for visualization, packaging each transformation into separate Lambda functions increases complexity. Using AWS Step Functions for workflow automation adds overhead and might not be as efficient as exporting the entire workflow to a SageMaker pipeline.
As I see it, the answer is:
Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Use SageMaker Data Wrangler templates for visualization. Export the feature processing workflow to a SageMaker pipeline for automation.