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.

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