The guide to saving time when creating AI data
Wading through vast amounts of unstructured data to accurately annotate assets requires a tremendous amount of patience, organization, and time. Drawing from the experiences of hundreds of AI teams across industries, we are sharing six time-saving practices for ML teams to implement when handling AI data. In this guide, you’ll discover how to:
Annotate faster with a dynamic queueing system
Improve communication, collaboration, and consensus between teams
Utilize a programmatic-approach for quicker access to data
Leverage software optimized for speed
Incorporate automation through model-assisted labeling
Utilize active learning and prioritize the right data
These strategies will unblock key barriers and speed up overall processes and capabilities for a quicker path to production AI.
