To transition into an ML Engineer role, women should deepen their programming skills beyond data analysis. This includes mastery of languages like Python, Java, or C++, writing clean, modular code, and understanding software development best practices such as version control (Git), testing, and debugging. Building proficiency in software engineering principles helps in developing scalable machine learning applications.

To transition into an ML Engineer role, women should deepen their programming skills beyond data analysis. This includes mastery of languages like Python, Java, or C++, writing clean, modular code, and understanding software development best practices such as version control (Git), testing, and debugging. Building proficiency in software engineering principles helps in developing scalable machine learning applications.

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