Designing the optimal iteration loop for AI data

ML practitioners have realized that iteration is the key to performant models, but perfecting and tightening the loop still remains a challenge for even the most advanced teams.

Watch this session to hear the practical insights on how to get models to production-level performance quickly, how to best structure your training data pipeline and create the optimal iteration loop for production AI.

Learn how to:

Visualize model errors and better understand where performance is weak so you can more effectively guide training data efforts
Identify trends in model performance and quickly find edge cases in your data
Reduce costs by prioritizing data labeling efforts that will most dramatically improve model performance
Improve collaboration between domain experts, data scientists, and labelers

Watch the Webcast Recording

Just some of companies that Labelbox is working with to build AI applications:

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Manu Sharma

CEO & Cofounder, Labelbox

Featured Speakers

Matthew McAuley

Senior Data Scientist, Allstate

Just some of companies that Labelbox is working with to build AI applications:

Kyle Wiggers

AI Staff Writer, VentureBeat

Designing the optimal iteration loop for AI data