How do you manage feature engineering at scale in Azure ML?

Feature engineering at scale is managed using reusable pipelines, feature stores, distributed processing, versioning, and centralized governance.

Feature engineering is often the most time-consuming phase of machine learning development. At enterprise scale, organizations require standardized, reusable, and governed feature pipelines.

Azure ML supports scalable feature engineering through:

Feature stores centralize reusable features and maintain consistency between training and inference environments.…