In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support plan. Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage.
In this series of posts, we share lessons learned about optimizing costs in Amazon SageMaker. In this post, we focus on data preprocessing using Amazon SageMaker Processing and Amazon SageMaker Data Wrangler jobs.
Data preprocessing holds a pivotal role in a data-centric AI approach. However, preparing raw data for ML training and evaluation is often a tedious and demanding task in terms of compute resources, time, and human effort. Data preparation commonly needs to be integrated from different sources and deal with missing or noisy values, outliers, and so on.
Furthermore, in addition to common extract, transform, and load (ETL) tasks, ML teams occasionally require more advanced capabilities

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