What's the Headache with Data For ML❓

What is data for ML?

In the industry this is also called: "model-ready data". The key difference between data for ML vs. others is this "readiness", which is typically prepared through preprocessing pipelines, feature transformations and engineering. These pipelines can be part of the models (sklearn preprocessing), or ETL steps (Spark, DBT, Airflow).

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