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Building and managing machine learning (ML) features at scale is one of the most critical and complex challenges in modern data science workflows. Organizations often struggle with fragmented feature pipelines, inconsistent data definitions, and redundant engineering efforts across teams. Without a centralized system for storing and reusing features, models risk being trained on outdated or mismatched data, leading to poor generalization, lower model accuracy and governance issues. Furthermore, enabling collaboration across data engineering, data science, and ML operations teams becomes difficult when each group maintains its own isolated datasets and transformations.…

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