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Agile Data Labeling
Jan 20th, 2021 @ 2PM PST
Data and AI have never been more essential to businesses of all sizes, across every industry around the world. To become truly data-driven, organizations need to provide decision-makers with labeled data that’s relevant to their analyses. In order to deliver impactful ML applications, organizations need to be able to efficiently create training data.
The growing frustration among data scientists is driving the need for the Agile Manifesto of Data Labeling.
A successful Agile Manifesto of Data Labeling should begin with the same principle of reactivity. Successful preparation of training data involves cooperation, feedback, and discipline.
Shayan Mohanty is the CEO and Co-Founder of Watchful, a company that largely automates the process of creating labeled training data. He has a decade leading data engineering teams at various companies including Facebook, where he served as lead for the stream processing team responsible for processing 100% of the ads metrics data for all FB products. He is also a Guest Scientist at Los Alamos National Laboratory and has given talks on topics ranging from Automata Theory to Machine Teaching.
Jennifer Prendki is the CEO of Alectio, a disruptive B2B AI company pioneering the concept of DataPrepOps. Previously, she was the VP of Machine Learning at Figure Eight–the company that pioneered data labeling–the Chief Data Scientist at Atlassian and a Senior Manager of Data Science in the Search Team at Walmart. She has spent most of her career building and scaling high performing machine learning teams and developing data-driven cultures. She was trained as a scientist with a PhD in Particle Physics from Sorbonne University.
All attendees will:
- Gain a basic understanding of the principles of agile data labeling
- Learn about how agile data labeling can be applied to your machine learning pipelines
- Explore and discover two new platforms that help you and your enterprise transform the way you label data