How to Setup Data Annotation Pipelines

Setting up a high-quality data annotation pipeline is a critical and complex investment for any machine learning project. The process involves significant decisions around workforce management, such as how to hire, train, and incentivize human annotators for consistent results. To ensure data quality, it is essential to establish robust review mechanisms like consensus algorithms or expert reviewers and to maintain a "golden dataset" for performance monitoring. Teams must also choose between building custom in-house annotation tools or using commercial software, considering factors like data type and the availability of ML-assisted features. These challenges often lead companies to leverage external services to manage the entire data labeling workflow.