This article is part of the AWS SageMaker series on exploring ML strategy questions for Fortune 500 companies. It focuses on Data Transformation and Feature Engineering using AWS SageMaker. The article discusses key questions related to automation, collaboration, reproducibility, and governance, and provides examples of how AWS SageMaker can address them. It also covers the use case of Fraud Detection and demonstrates how to use SageMaker Wrangler for data transformation and SageMaker Feature Store for data storage and sharing. The article concludes by mentioning the ability to track data lineage and store transformation code using SageMaker Lineage and Git integration, respectively. Finally, it mentions the option to apply transformation steps in real-time using SageMaker pipelines.

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