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.

For practical insights on how to get models to production-level performance quickly, join industry leaders for a discussion on how to best structure your training data pipeline and create the optimal iteration loop for production AI.
  • 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
Matthew McAuley
Senior Data Scientist, Allstate
Dr. Matthew McAuley is a Senior Data Scientist at Allstate, working in the D3 (Data, Discovery & Decision Science) department. He leads the Unstructured Data Science team, building machine learning models to enrich unstructured data, with a focus on identifying and securing personal information.
Manu Sharma
CEO & Cofounder, Labelbox
Manu Sharma is an engineer, designer and entrepreneur. He is the Founder & CEO of Labelbox, the industry leading training data software that is accelerating global access to artificial intelligence. For over 6 years, Manu has designed and built products used by thousands of organizations at highly transformative companies such as Planet Labs and DroneDeploy. His interests range from artificial intelligence to clean energy, aviation and space exploration.
Kyle Wiggers
AI Staff Writer, VentureBeat
Kyle Wiggers is a senior AI correspondent at VentureBeat, where he writes about machine learning and automation. Covering emerging technologies and research in AI, his work explores the implications of developments in the fields of computer vision, natural language processing, sentiment analysis, and more.
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