Organizations are using machine learning (ML) and AI services to enhance customer experience, reduce operational cost, and unlock new possibilities to improve business outcomes. Data underpins ML and AI use cases and is a strategic asset to an organization. As data is growing at an exponential rate, organizations are looking to set up an integrated, cost-effective, and performant data platform in order to preprocess data, perform feature engineering, and build, train, and operationalize ML models at scale. To achieve that, AWS offers a unified modern data platform that is powered by Amazon Simple Storage Service (Amazon S3) as the data lake with purpose-built tools and processing engines to support analytics and ML workloads. For a unified ML experience, you can use Amazon SageMaker Studio, which offers native integration with AWS Glue interactive sessions to perform feature engineering at scale with sensitive data protection. In this post, we demonstrate how to

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