Data operations teams play a critical role in computer vision projects by managing tasks such as data sourcing, cleaning, annotation, and labeling to ensure high-quality datasets are fed into machine learning models. Unlike machine learning operations, data ops focus on maintaining an efficient data pipeline, employing automation and strategic use of software tools to streamline processes. They are responsible for quality control, compliance, and ensuring the data is free from bias and errors, which is vital for the model's performance. These teams utilize best practices from software development, like CI/CD and unit testing, to enhance operational efficiency and collaboration among stakeholders. The choice of powerful, feature-rich annotation tools is crucial for successful data operations, which ultimately support high-performance computer vision models by treating data as a valuable intellectual property asset.