Managing Data Labeling Ops for Success with Audrey Smith
EPISODE 583
|
JULY
18,
2022
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About this Episode
Today we continue our Data-Centric AI Series joined by Audrey Smith, the COO at MLtwist, and a recent participant in our panel on DCAI. In our conversation, we do a deep dive into data labeling for ML, exploring the typical journey for an organization to get started with labeling, her experience when making decisions around in-house vs outsourced labeling, and what commitments need to be made to achieve high-quality labels. We discuss how organizations that have made significant investments in labelops typically function, how someone working on an in-house labeling team approaches new projects, the ethical considerations that need to be taken for remote labeling workforces, and much more!
About the Guest
Audrey Smith
MLtwist
Resources
- MLtwist
- How the AI industry profits from catastrophe | MIT Technology Review
- Data Debt in Machine Learning with D. Sculley - #574
- Principle-Centric AI with Adrien Gaidon - #575
- The Fallacy of “Ground Truth” with Shayan Mohanty - #576
- Feature Platforms for Data-Centric AI with Mike Del Balso - #577
- Data-Centric AI Panel Discussion
