Regularly collecting data on gender representation, pay gaps, and workplace experiences enables organizations to identify bias patterns. Using this data to inform policies and track progress is critical to creating lasting change in ML engineering careers.

Regularly collecting data on gender representation, pay gaps, and workplace experiences enables organizations to identify bias patterns. Using this data to inform policies and track progress is critical to creating lasting change in ML engineering careers.

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