Labeling training data is the one step in the data pipeline that has resisted automation. It’s time to change that.

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Webinar: Sep 2, 2021, 3 pm PT, 6 pm ET

 

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The significant issues with hand labeling include the introduction of bias (and hand labels are neither interpretable nor explainable), the prohibitive costs (both financial costs and the time of subject matter experts), and the fact that there is no such thing as gold labels (even the most well-known hand labeled datasets have label error rates of at least 5%!).

Shayan Mohanty, CEO of Watchful, joins Hugo Bowne-Anderson, Head of Data Science Evangelism at Coiled, to discuss why hand labeling, a fundamental part of human-mediated machine intelligence, is naive, dangerous, and expensive. He will share the ever-growing world of alternatives, which includes semi-supervised learning, weak supervision, and active learning. Hand labeling in the age of these other technologies is akin to scribes hand-copying books post-Gutenberg.

All attendees will:

  • Learn about techniques and approaches to scale data annotation
  • Receive a Jupyter Notebook that will guide the attendee through some of the concepts discussed
  • Receive a one-page summary of the key-concepts covered after the webinar

Register here



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