Unlike data analysts, machine learning engineers work extensively with software development pipelines. Skills in version control (e.g., Git), writing clean, modular, and testable code, and knowledge of software development lifecycle processes are essential. Understanding containerization (Docker) and continuous integration/deployment (CI/CD) will help in productionizing machine learning models.
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