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.