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preprocessing-data-with-automated-pipelines

separate

Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.

When to use

• Prepare raw data for machine learning models.
• Automate data cleaning and transformation processes.
• Implement a robust ETL (Extract, Transform, Load) pipeline.

When not to use

• Whole-task ML ownership
• Leakage and prediction-time auditing
• Grouped scientific preprocessing with stronger methodological constraints

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