A strong data engine involves the creation of large volumes of high-quality training data. Delays from quality management or the lack of metrics and insight into labeling quality, throughput, and efficiency during the labeling process can significantly hinder model development.
In order to maximize efficiency and cost while ensuring labeling quality, ML teams require strong data management, quality and performance monitoring, and advanced techniques to improve the speed and efficiency of their labeling operations.
AI team managers and administrators are able to track live analytics throughout their labeling projects to monitor quality, throughput, and efficiency. With Labelbox, teams can drill further into metrics on individual labeler progress and performance on labeling time, review and rework time, total time spent, and more.
As one of the primary tools for managing your labeling operations in a Labelbox project, you have a holistic view of your project's throughput, efficiency, and quality throughout the labeling process.
The Throughput view provides insight into the amount of labeling work being produced. It can help provide answers to questions such as:
The Efficiency view provides insight into the time spent per unit of work. It can help provide answers to questions such as:
The Quality view provides teams with insight into the accuracy and consistency of the labeling work being produced. It can help provide answers to questions such as:
Each of the above metrics are reported at the overall project level and at the user level, giving you a single source of truth for your project's annotation and labeler analytics.
Learn more about the project performance dashboard in our documentation.
For larger Enterprise teams, Labelbox provides advanced analytics to better track labeling time and spend.
Learn more about advanced analytics for Enterprise teams in our documentation.