Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training.
Feature quality is critical to ensure a highly accurate ML model. Transforming raw data into features using aggregation, encoding, normalization, and other operations is often needed and can require significant effort. Engineers must manually write custom data preprocessing and aggregation logic in Python or Spark for each use case.
This undifferentiated heavy lifting is cumbersome, repetitive, and error-prone. The SageMaker Feature Store Feature Processor reduces this burden by automatically transforming raw data into aggregated features suitable for batch training ML models. It lets engineers provide simple data transformation functions, then handles running them at scale on Spark and managing the underlying infrastructure. This enables data scientists

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