Building and maintaining data pipelines is a critical skill for machine learning engineers. This involves extracting, transforming, and loading data (ETL), working with large datasets, and ensuring data quality and consistency. Familiarity with tools like Apache Spark, Airflow, or Kafka can be incredibly valuable when handling production-scale data workflows.
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