Practical AI Trends in data labeling
Sep 14, 2021
Michael Malyuk from the Label Studio team shares insights on the evolving landscape of data labeling. He emphasizes how organizations are now viewing labeling as a strategic asset. The discussion touches on the influence of the pandemic on labeling practices and the significance of quality metrics. Malyuk also explores advancements in open-source tools like Label Studio, highlighting features that support various data types. The conversation wraps up with thoughts on the future of labeling, including the integration of human oversight and exciting new capabilities like video.
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Data Labeling as a Liquid Asset
- Companies now realize data labeling is key to making raw data a liquid asset.
- They now invest more in labeling processes, reports, analytics, and metrics.
Labeling's Strategic Importance
- The ML industry is maturing, shifting from just getting labeling done to recognizing its strategic value.
- Labeling is now seen as a source of competitive advantage, unlike readily available GPUs.
Building a Data Labeling Culture
- Establishing best practices for labeling tasks is crucial, including clear instructions and minimizing variability.
- Different groups may need tailored onboarding and guidance, adding complexity to the process.