Snorkel AI Announces Speaker Line-Up for The Future of Data-Centric AI Event on September 28

Snorkel AI, a data-centric AI platform company powered by programmatic data labeling, today announced the speaker line-up for The Future of Data-Centric AI event for leaders of data science, ML engineering and analytics teams, practitioners, visionaries, researchers and students.

“As models have become increasingly powerful and commoditized but also data-hungry, the success or failure in AI most often depends on the training data As a result, AI development is shifting from being model-centric to data-centric,” said Alex Ratner, co-founder and CEO of Snorkel AI. “With The Future of Data-Centric AI, our goal is to bring the AI community together to share transformative ideas and new research about the data-centric approach and its vital role in making AI practical.”

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Speakers include:

  • Alex Ratner, Co-founder and CEO, Snorkel AI
  • Andrew Ng, DeepLearning.AI; Founder and CEO, Landing AI
  • Anima Anandkumar, Director of ML Research, Nvidia
  • Ce Zhang, Assistant Professor, ETH Zurich
  • Chelsea Finn, Assistant Professor, Stanford University
  • Chris Ré, Associate Professor, Stanford AI Lab
  • Darío García-García, Director of ML Research, Netflix
  • Imen Grida Ben Yahia, Program Manager/Tech Lead, Orange
  • James Zou, Assistant Professor, Stanford University
  • Justin Gottschlich, Principal AI Scientist and Director/Founder, Machine Programming Research, Intel
  • Michael DAndrea, Principal Data Scientist, Genentech
  • Roshni Malani, Engineering Leadership, Snorkel AI
  • Sharon Li, Assistant Professor, University of Wisconsin, Madison
  • Skip McCormick, Data Science Fellow, BNY Mellon
  • Xu Chu, Assistant Professor, Georgia Institute of Technology

The event will explore the shift from a model-centric practice to a data-centric approach to building AI and discuss challenges, solutions and ideas to make AI practical, both now and in the future. Topics covered include:

  • Interactive development of ML pipelines
  • MLOps desiderata & design principles
  • Auto-labeling
  • Weak supervision
  • Data cleaning and augmentation
  • Fine-grained error analysis
  • Model monitoring
  • Training data auditability
  • Data-centric AI case studies

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