Data labeling operations are crucial for the success of computer vision projects as they oversee the full lifecycle of data labeling and annotation, from sourcing and cleaning to training and making a model production-ready. A well-established data labeling operations team plays a mission-critical role in implementing AI projects, especially when they are data-centric. While automated tools can help with labeling and annotation, having an effective process and team is essential for ensuring high-quality training data. The benefits of data labeling operations include improved accuracy and performance of machine learning models, reduced time and cost, and improved quality control. When deciding whether to buy or build a data labeling tool, it's often more cost-effective to purchase a commercial platform, which can be up and running in days compared to months. To create successful data labeling operations, teams need to understand the use case, establish documented workflows and processes, and implement iterative feedback loops and quality assurance. With the right tools and approach, data labeling operations can be scaled effectively, leading to improved model performance and faster time-to-production AI.