It's quick and easy to start annotating data using locally installed tools. For most simple annotation tasks being performed by a single labeler, this solution architecture works well. As data labeling needs scale, data management and quality control processes are needed to produce accurate and consistent training data. A common cause of underperforming AI systems is low accuracy training data.
Developing expert human intelligence requires training by experts, and this also applies to training expert artificial intelligence. Achieving compelling AI performance is preceeded by numerous experimentation and optimization cycles. Rapid deployment of expert AI systems depends on mature data labeling infrastructure capable of producing training data that is consistent and accurate. When building data labeling infrastructure, consider the following:
Data labeling services provide cost efficient access to labor pools. The advantage of this is quick turnaround of labeled data at low per label costs. The performance of your AI system is determined by both the accuracy and quantity of training data. If a data labeling service does have the requisite domain expertise to label your data, make sure to quantify the labeling accuracy needed and communicate these requirements to the labeling service.
Creating accurate and consistent training data requires a set of integrated tools that enable your cross functional team of engineers, labelers and managers to collaborate effectively. When buying a data labeling solution, priotitize the following:
Labelbox is an enterprise-grade data labeling platform for building expert artificial intelligence. Every day, hundreds of teams use Labelbox to create and manage high quality training data.
Labelbox provides comprehensive value right from the start, including: