Practical Approaches to Training Data Strategy

Summit Track: 
Technical Insights I

Recent McKinsey research cites the top five limitations that prevent companies from adopting AI technology. Training data strategy is a common thread. Companies face challenges obtaining enough AI training data, developing strategies for robust data quality and ensuring that bias does not occur. Are you struggling to get results from your algorithm due to an inefficient training data strategy? In this presentation, you will learn best practices for developing a training data strategy so your algorithms perform as intended. We'll discuss how to define effective training data rules and how to evolve these rules as your algorithm learns. We'll explore how to determine how much training data you really need, and how to diversify your data set to cover edge cases and avoid bias. Finally, we'll explain how to understand where your organization sits on the training data maturity continuum.


Audrey Jill Boguchwal

Senior Product Manager, Samasource

Audrey is passionate about the intersection of ethics and artificial intelligence. As a trained anthropologist, she is fascinated by understanding how humans interact with technology and how we can use technology for good. Currently a Senior Product Manager at Samasource, a provider of training data for the world’s leading AI technologies, she guides cross-functional teams to create thoughtful product solutions. She has guided teams of designers and engineers at HUGE Inc. and NBCUniversal, and monitored user analytics at the Wall Street Journal. With a unique background in anthropology and an MBA, she is able to balance the voice of the customer with the goals of the business. Audrey is also passionate about using her digital technology and data analytics skills to facilitate social impact and environmental solutions through technology. Audrey holds a BA in history from Harvard, an MA in anthropology from Columbia and an MBA from UNC Chapel Hill KFBS.

See you at the Summit! May 20-23 in Santa Clara, California!
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