Data scientists and machine learning (ML) engineers often prepare their data before building ML models. Data preparation typically includes data preprocessing and feature engineering. You preprocess data by transforming data into the right shape and quality for training, and you engineer features by selecting, transforming, and creating variables when building a predictive model.
Amazon SageMaker helps you perform these tasks by simplifying feature preparation with Amazon SageMaker Data Wrangler and storage and feature serving with Amazon SageMaker Feature Store. You can prepare your data and engineer features using over 300 built-in transformations with Data Wrangler. Then you can persist those features to a purpose-built feature store for ML with Feature Store. These services help you build automatic and repeatable processes to streamline your data preparation tasks, all without writing code.
We’re excited to announce a new capability that seamlessly integrates Data Wrangler with Feature Store. You can now easily create

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