A solid understanding of data pipelines, data ingestion, and transformation is crucial. Focus on tools like Apache Kafka, Apache Airflow, and ETL frameworks. Proficiency in SQL and NoSQL databases, as well as experience with big data platforms (Hadoop, Spark), enables ML engineers to handle data more efficiently and ensure the availability of high-quality data streams for modeling.
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