Developing the capacity to annotate massive volumes of data while maintaining quality is a function of the model development lifecycle that enterprises often underestimate. It’s resource intensive and requires specialized expertise that very few data science teams possess. At Alegion, we’ve established quality management best practices based on our experience labeling tens of millions of images, video, text, and audio records. Below, we share our approach to improving data labeling quality at scale.
Quality Management Best Practices:
Quality Management Methodology
Task and Workflow Design
Workforce Training and Skills Scoring
Labeling Tools and User Interface Configuration
Download a complimentary copy of this short guide to improve your data labeling quality.