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
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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:

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Kyle Wiggers

AI Staff Writer, VentureBeat

Designing the optimal iteration loop for AI data